Efficient 3D Morphable Face Model Fitting

Article (PDF Available)inPattern Recognition 67:366-379 · February 2017with 990 Reads
DOI: 10.1016/j.patcog.2017.02.007
3D face reconstruction of shape and skin texture from a single 2D image can be performed using a 3D Morphable Model (3DMM) in an analysis-by-synthesis approach. However, performing this reconstruction (fitting) efficiently and accurately in a general imaging scenario is a challenge. Such a scenario would involve a perspective camera to describe the geometric projection from 3D to 2D, and the Phong model to characterise illumination. Under these imaging assumptions the reconstruction problem is nonlinear and, consequently, computationally very demanding. In this work, we present an efficient stepwise 3DMM-to-2D image-fitting procedure, which sequentially optimises the pose, shape, light direction, light strength and skin texture parameters in separate steps. By linearising each step of the fitting process we derive closed-form solutions for the recovery of the respective parameters, leading to efficient fitting. The proposed optimisation process involves all the pixels of the input image, rather than randomly selected subsets, which enhances the accuracy of the fitting. It is referred to as Efficient Stepwise Optimisation (ESO).
Efficient 3D Morphable Face Model Fitting
Guosheng Hua,1, Fei Yana, Josef Kittlera, William Christmasa,, Chi Ho Chana,
Zhenhua Fenga, Patrik Hubera
aCentre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK
3D face reconstruction of shape and skin texture from a single 2D image can be per-
formed using a 3D Morphable Model (3DMM) in an analysis-by-synthesis approach.
However, performing this reconstruction (fitting) efficiently and accurately in a general
imaging scenario is a challenge. Such a scenario would involve a perspective camera to
describe the geometric projection from 3D to 2D, and the Phong model to characterise
illumination. Under these imaging assumptions the reconstruction problem is nonlin-
ear and, consequently, computationally very demanding. In this work, we present an
efficient stepwise 3DMM-to-2D image-fitting procedure, which sequentially optimises
the pose, shape, light direction, light strength and skin texture parameters in separate
steps. By linearising each step of the fitting process we derive closed-form solutions
for the recovery of the respective parameters, leading to efficient fitting. The proposed
optimisation process involves all the pixels of the input image, rather than randomly
selected subsets, which enhances the accuracy of the fitting. It is referred to as Efficient
Stepwise Optimisation (ESO).
The proposed fitting strategy is evaluated using reconstruction error as a perfor-
mance measure. In addition, we demonstrate its merits in the context of a 3D-assisted
2D face recognition system which detects landmarks automatically and extracts both
holistic and local features using a 3DMM. This contrasts with most other methods
which only report results that use manual face landmarking to initialise the fitting.
Our method is tested on the public CMU-PIE and Multi-PIE face databases, as well
Corresponding author: William Christmas
Email address: w.christmas@surrey.ac.uk (William Christmas)
1Current address: AnyVision, Queen’s Road, Belfast, BT3 9DT, UK
Preprint submitted to Elsevier December 13, 2016
as one internal database. The experimental results show that the face reconstruction
using ESO is significantly faster, and its accuracy is at least as good as that achieved
by the existing 3DMM fitting algorithms. A face recognition system integrating ESO
to provide a pose and illumination invariant solution compares favourably with other
state-of-the-art methods. In particular, it outperforms deep learning methods when
tested on the Multi-PIE database.
Keywords: face recognition; face reconstruction; 3D Morphable Model
1. Introduction
The intrinsic properties of 3D faces give scope for a representation that is immune
to the kinds of variations in face appearance that are introduced by the imaging pro-
cess such as viewpoint, lighting and occlusion. These invariant facial properties are
potentially useful in a wide variety of applications in computer graphics and vision.
However, recovering the 3D face and scene properties (viewpoint and illumination)
from the appearance conveyed by a single 2D image is very challenging. Specifically,
as noted in [1], it is impossible to distinguish between texture and illumination effects
unless some assumptions are made to constrain them both. The 3D morphable face
model (3DMM) [2] encapsulates prior knowledge about human faces that can be used
for this purpose, and therefore potentially it is a good tool for 3D face reconstruction.
The 3DMM is a concise statistical model of a 3D face population created from 3D
face data using principal component analysis (PCA). The model separately represents
the face shape and surface texture. PCA removes data correlation and identifies a small
number of latent variables which represent each face instance very efficiently. See also
[3, 4] for other developments of generative models applicable to 3D graph structures.
The reconstruction of a 3D face is conducted by a 3DMM fitting process, which
estimates the 3D shape, texture, pose and illumination from a single 2D input image.
Considerable research has been carried out to achieve efficient and accurate fitting. The
methods advocated in the literature can be classified into two categories:
1. Simultaneous Optimisation (SimOpt): All the parameters (shape, texture, pose
and illumination) are optimised simultaneously [2, 5, 6, 7];
2. Sequential Optimisation (SeqOpt): These parameters are optimised sequentially
[8, 9, 10].
The SimOpt algorithms use gradient-based methods which are often slow and can easily
get trapped in local minima. On the other hand, SeqOpt methods can have closed-form
solutions for some or all of the parameters, and accordingly have the potential to be
much more efficient computationally. However, the existing SeqOpt methods [8, 9,
10] make strong assumptions about the imaging camera and consequently they do not
generalise well to faces distorted by perspective effects.
In this work we introduce a novel SeqOpt fitting framework, referred to as Effi-
cient Stepwise Optimisation (ESO), which overcomes these problems and is an order
of magnitude faster than existing methods. This framework groups the parameters to be
optimised into 5 categories: camera model (pose), shape, light direction, light strength
and albedo (skin texture). The fitting is decomposed into two separate processes: geo-
metric fitting and photometric fitting.
Geometric Model Fitting Existing fast pose and shape fitting methods assume an
affine camera model [9, 10] which is adequate provided the object’s depth is small
compared with its distance from the camera. A rule of thumb is that the object should
be at least 10 times further from the camera than its depth. However, this is often not
the case, e.g. when using a laptop camera for video conferencing, and for authentica-
tion, or when a camera is mounted on a vehicle windscreen for driver authentication,
or monitoring the driver for tiredness. In such applications it is essential to relax the
assumption and adopt a more general perspective camera model, which renders the re-
construction problem nonlinear, and consequently computationally expensive. In order
to address this conundrum, we propose a novel approach to the shape fitting problem
by formulating the fitting cost function in 3D, rather than the usual 2D. This formu-
lation admits linearisation of the optimisation task which significantly enhances the
computational efficiency.
As in [7] the occluding face contour is used to improve the shape fitting accuracy.
In order to mitigate the additional processing costs, we propose to use landmarks on
the occluding face contour (see Section 4.3) instead of face contour edges to refine
the camera and shape estimates. To this end, we develop a method that automatically
establishes the correspondence between the occluding contour landmarks of the input
image and vertices of the 3D face model.
Photometric Model Fitting Both Phong [2, 7] and Spherical Harmonics models
[9, 10] have been used in the past to estimate the illumination parameters. However,
in order to model adequately both diffuse light and specularity, the latter method re-
quired many bases (81 in total) of spherical harmonics. Compared with the Spherical
Harmonics approach, the Phong model has a more compact representation (elaborated
further in Section 3), and is therefore used here. We found that it was adequate to
model the illumination as a combination of a single distant point source plus uniform
ambient light, thus keeping the number of coefficients to be found to a minimum.
To accelerate the light model fitting and skin texture parameter estimation, we
present a novel approach to optimise both Phong model parameters and albedo. Specif-
ically, we propose techniques (Section 4) to linearise the Phong model and the subse-
quent albedo estimation. Because the objective functions of these linear methods are
convex, globally optimal solutions are guaranteed.
The measures to accelerate the illumination and texture reconstruction proposed in
the paper speed up the fitting process by a factor of ten or more. We evaluate the fitting
accuracy and show that it is superior to that achieved by the current alternatives. The
impressive performance is the consequence of the ESO fitting process involving all the
model vertices simultaneously, rather than just a randomly sampled subset.
We also evaluate the ESO fitting algorithm as part of a fully automatic pose- and
illumination-invariant face recognition system. Its performance is at least compara-
ble to the best performing state of the competitors, including solutions based on deep
learning methods [11, 12] when evaluated on the Multi-PIE dataset.
The paper is organised as follows. In the next section we present a brief sum-
mary of the related work. The fitting problem is formulated in Section 3 to establish
a methodological baseline. Our fast fitting algorithm ESO is developed in Section 4.
The proposed algorithm is evaluated in Section 5 in terms of its reconstruction perfor-
mance, as well as when embedded in a face recognition system. Section 6 draws the
shape parameters
shape parameters
texture parameters
texture parameters
camera parameters
camera parameters
lighting parameters
lighting parameters
face synthesis
face recognition
face synthesis
face recognition
Figure 1: 3D morphable model fitting pipeline including the inputs and outputs of a fitting, and the applica-
tions of the fitting outputs.
paper to a conclusion.
2. Related Work on 3D Morphable Model Fitting
The 3DMM, first proposed by Blanz and Vetter [2], has successfully been applied
to computer vision and graphics. A 3DMM consists of separate face shape and texture
models learned from a set of 3D exemplar faces. These faces are represented as a
graph, in which the node attributes are the 3D position and RGB colour at that node,
and the edges indicate geometric connectivity. Related work (e.g. [13]) considers more
complex image data. By virtue of a fitting process, a 3DMM can recover the face (shape
and texture) and scene properties (illumination and camera model) from a single 2D
image in a process schematically summarised in Fig. 1. The recovered parameters can
be used for different applications, such as realistic face synthesis and face recognition.
However, it is well known that achieving accurate fitting is particularly difficult for
two reasons. Firstly, when recovering the 3D shape from a single 2D image, the 3D
shape is generally projected to 2D in order to compare it with the 2D image features.
As a result, the depth information of the 3D shape is lost. Secondly, separating the
contributions of albedo and illumination is an ill-posed problem [14, 15]. Motivated
by the above challenges, considerable research [2, 6, 7, 8, 9, 10] has been carried out
to improve the fitting performance in terms of efficiency and accuracy. As mentioned
in Section 1, these methods can be classified into two groups: SimOpt and SeqOpt.
In the SimOpt category, the fitting algorithm in [2, 5] minimises the sum of squared
differences over all colour channels and all pixels between the input and reconstructed
images. A Stochastic Newton Optimisation (SNO) technique is used to optimise a non-
convex cost function. Performance of this technique is poor in terms of both efficiency
and accuracy because it is an iterative gradient-based optimiser which may end up in a
local minimum.
The efficiency of optimisation is the driver behind the work of [6] where an Inverse
Compositional Image Alignment algorithm [6] is introduced for fitting. The fitting is
conducted by modifying the cost function so that its Jacobian matrix can be regarded
as constant. In this way, the Jacobian matrix is precomputed, which greatly reduces the
computational costs. However, this method cannot model illumination effects.
The Multi-Feature Fitting (MFF) strategy [7] is known to achieve the best fitting
performance of the SimOpt methods. It makes use of many complementary features
from an input image, such as edges and specularity highlights, to constrain the fit-
ting process. The advantages of using these features are demonstrated in [7]. Further
improvements to the MFF framework have been achieved by enhancing the fitting ro-
bustness to varying image resolution with a resolution-aware 3DMM [16], and by de-
ploying a facial symmetry prior in [15] to ameliorate the quality of illumination fitting.
However, all the MFF-based fitting methods are rather slow.
In the SeqOpt category, the ‘linear shape and texture fitting algorithm’ (LiST) [8]
was proposed for improving fitting efficiency. The idea is to update the shape and tex-
ture parameters by solving linear systems. However, the illumination and camera pa-
rameters are optimised by the gradient-based Levenberg-Marquardt method, exhibiting
many local minima. The experiments reported in [8] show that the fitting is of similar
accuracy to the SNO algorithm, but much faster, in spite of the shape being recovered
using a relatively slow optical flow algorithm. The drawback of this approach is the
prerequisite that the light direction is known before fitting, which is not realistic for
automatic analysis.
Another SeqOpt method [9] decomposes the fitting process into geometric and pho-
tometric parts. The camera model is optimised by the Levenberg-Marquardt method,
and shape parameters are estimated by a closed-form solution. In contrast to the pre-
vious work, this method recovers 3D shape using only facial feature landmarks, and
models illumination using spherical harmonics. Illumination and albedo are deter-
mined using least squares optimisation. The work in [17] improved the fitting perfor-
mance of [9] by segmenting the 3D face model into different subregions. In addition,
a Markov Random Field is used in [17] to model the spatial coherence of the face tex-
ture. However, the illumination models of [9, 17] cannot deal with specular reflectance
because only 9 low-frequency spherical harmonics bases are used. In addition, [9, 17]
use an affine camera model, which cannot model perspective effects.
In common with [9], two more recent SeqOpt methods [10, 18] also sequentially fit
geometric and photometric models using least squares. Both methods use only facial
landmarks to estimate pose and facial shape via an affine camera. They also share the
use of spherical harmonics models to estimate illumination. The authors in [18] use 9
spherical harmonics bases, which cannot model specularity. The method in [10] can
model specularity by projecting the RGB values of the model and input images to a
specularity-free space for diffuse light and texture estimation. The specularity is then
estimated in the original RGB colour space. In common with [9], both methods [10,
18] use an affine camera, which cannot model perspective effects. In addition, the
colour of lighting in [10] is assumed to be known, which limits the applicability of the
Some works only focus on shape fitting [19, 20, 21]. In [20], around 100 facial
landmarks are used to recover the facial shape employing the Levenberg-Marquardt
algorithm as the optimiser. In contrast to [20], [19] uses local image features rather
than facial landmarks as these features are more robust.
3. 3D Morphable face model and face image rendering
A 3D face model is a representation of the surface of a class of objects — the objects
in our case being faces. Each face consists of a set of vertices whose positions in 3D
space collectively express the face shape. The vertices also each have an RGB pixel
value, that collectively express the face skin texture (albedo). The model describes both
the shape of a face and its appearance, determined by the surface texture. It is defined
by a mesh of vertices V={vi|i= 1, ...., n}, sampling the face surface at a predefined
set of facial points of semantic identity (eye corners, nose tip, etc). The ith vertex vi
of a face is located at wi= (xi, yi, zi)T, and has the RGB colour values (ri, gi, bi).
Hence a 3D face is represented in terms of shape and texture as a pair of vectors:
s= (x1, y1, z1, ......, xn, yn, zn)T,t= (r1, g1, b1, ......, rn, gn, bn)T(1)
Even for twins, faces are unique. Each individual will have a particular face shape
and skin characteristics. The variability of face shape and skin texture in a population of
individuals is captured by a statistical 3D face model, defined by a probability distribu-
tion in the sand tspace. Since many vertex shape and texture measurements are highly
correlated, a population of 3D faces inevitably lies in a subspace of the sand tspace,
typically determined by the Principal Component Analysis (PCA) or other sparse rep-
resentation methods. Focusing on the former, let SR3n×rsand TR3n×rtdenote
the PCA bases of the rsshape and rttexture variations respectively. A face instance
(s,t)can concisely be expressed as
where s0and t0are the mean face shape and texture respectively. The parameters α
and βare assumed to have normal distributions:
p(α) N (0,σs)(3)
p(β) N (0,σt)(4)
where σsand σtare the vectors of variances of the latent model shape and texture
As the bases and the mean vectors are fixed for a particular population, all statisti-
cal information is conveyed by the parameter vectors αand β, the dimensionality of
which is considerably lower than that of the original face space. Each pair of model pa-
rameter vectors αand βdefines an instance of a 3D face. This provides a very concise
representation of the face, which is convenient from the point of view of face synthe-
sis. By changing the shape and texture parameters we can generate different faces. A
transition from one pair of parameter vectors to another pair will morph one face to
another in a smooth manner. This morphing capability of the statistical 3D face model
has given it its name as the 3D Morphable Face Model (3DMM).
A 3DMM can be used for many purposes in face analysis. For instance, the model
can be fitted to an input 2D face image, and the estimated shape and texture parameters
of the reconstructed 3D face used for face recognition in the face model parameter
space. Alternatively, given the pose of an input 2D face image, we can fit the 3DMM
to a gallery face image and use the fitted 3D face to synthesise a new pose of the
subject. For instance this could be a pose identical to the given pose in order to perform
matching. Another possibility is to fit the 3DMM to an input 2D image of arbitrary
pose, and then frontalise the query image with the help of the estimated 3D face shape.
The operative phrase in all these use cases is 3DMM fitting. It is the crucial prerequisite
enabling all these applications.
The underlying principle of fitting a 3DMM to an input 2D face image is to identify
the shape and texture parameters of the face model that would enable the synthesis of a
2D model image deemed indistinguishable from the input query image. However, the
rendering process is quite complex. It involves not only the selection of model shape
and texture parameters to produce a 3D face instance, but also its transformation to a
new pose, and the subsequent projection of the 3D face to 2D under a particular scene
illumination. The assessment of similarity of the synthesised image to the input image
also traditionally involves sampling the input image at 2D points corresponding to the
projection of the 3D mesh vertices onto the 2D input image.
Let us now describe the rendering process, the underlying physics of which is cap-
tured in Fig. 2 in more detail. We shall render a 2D view of a face instance showing
a particular pose by rotating and translating the camera with respect to the face model
coordinate system by a (3 ×3) rotation matrix Rand a 3D translation vector τrespec-
tively. In the camera coordinate system the transformed shape s0can be expressed in
matrix form as
s0=Us +ˇ
where Uis the block diagonal matrix with ncopies of the rotation matrix Ron its
diagonal, and ˇ
τis a vector composed of ncopies of the displacement τ.
),o yx
Camera coordinates
Image plane
dHead mesh
Light source
Figure 2: Physics of rendering: At image pixel position e
wi, the RGB value output by the camera (small green
blob) measures the reflection, on the face surface point wi, of the light source illuminating the face surface
in direction d. The surface normal at wiis ni. In the head (purple blob) coordinate system, the camera is
located at position τand the viewing direction of the vertex wiis vi. The specular light is reflected from
wicentred on direction ri, where riis such that the surface normal nibisects riand the direction dof the
incident light.
The pixel values at locations corresponding to swill depend on the albedo tand the
scene illumination. Different illumination models can be adopted for lighting the face
(e.g. [22]), but we will adopt the Phong model which can represent complex reflectance
phenomena, including specular reflectance, using a small number of parameters. The
appearance of the generated face at each point, represented by a 3n-dimensional vector
aM, is the product of the interplay of the face surface normal, skin albedo tand the in-
cident light, assumed to be the sum of contributions from ambient, diffuse and specular
+ (ˇ
| {z }
where the ambient light ˇ
lais a 3n-dimensional vector, composed of ncopies of the
ambient light intensity la= (lr
a, lg
a, lb
la= (lr
a, lg
a, lb
a, ........lr
a, lg
a, lb
Similarly, ˇ
ldis a 3n-dimensional vector, composed of ncopies of the directed light
strength ld= (lr
d, lg
d, lb
ld= (lr
d, lg
d, lb
d, ....., lr
d, lg
d, lb
The symbol denotes an element-wise multiplication operation. The matrix N3is a
stack of 3 copies of the matrices N:
where NRn×3is a stack of the surface normals niR3at vertices i= 1, ..., n
(see Fig. 2). Unit vector dR3is the light direction. Vector eR3nis a stack of the
specular reflectance eiof each vertex i= 1, ....., n (the components of which could be
different for the three channels), i.e.,
where viis the viewing direction of the ith vertex. Since in the face model coordinate
system the camera is at position τ, the viewing direction can be expressed as vi=
|τwi|where wi= (xi, yi, zi)Tis the vector of the 3D coordinates of that vertex.
Unit vector ridenotes the reflection direction of the light source at the ith vertex:
ri= 2hni,dinid. The two constants ksand γdenote the specular reflectance
and shininess respectively [23]. Note that ksand γare determined by the facial skin
reflectance property, which is similar for different people. They are assumed constant
over the whole facial region. For the sake of simplicity, in our work, we also assume
that ksand γare the same for the three colour channels. Thus each entry in eiis
repeated three times. In this work, the components of ksare each set to 0.175, and γis
set to 30, following [23].
In all cases it is important to check all vertices for visibility so that parts of the face
turned away from camera do not contribute to the rendered pixel values.
3.1. Fitting 3DMM to a 2D image
Let us consider an input face image Iacquired by a camera with focal length f
and the coordinates of its optical axis in the image plane o= (ox, oy)T. It is assumed
that the image is landmarked. Let ρdenote the set of extrinsic and intrinsic camera
ρ={R,τ, f, o}(11)
and let us lump together all illumination parameters as
µ={la,ld,d,ks, γ}(12)
Fitting the 3D face involves finding pose, shape, texture and illumination parameters
ρ, α,β, µ so that the image reconstructed from the model:
aM= (rM
1, gM
1, bM
1, ......., rM
n, gM
n, bM
is as close as possible to the input image.
Typically, the quality of the reconstruction is measured in 2D. This involves pro-
jecting the mesh of 3D vertices into 2D. For each vertex the camera projects the triplet
of its 3D coordinates into 2Dpixel location in the camera image plane as
where PR2n×3nis a block diagonal matrix constructed from the projection matrices
Pi, i = 1, ...n:
i0 0
Note that each Piis a function of the corresponding depth coordinate z0
i, as well as
the camera focal length f. The negative term in Piresults from an assumption of
a clockwise image coordinate system. The 2n-dimensional vector ˇ
ois a stack of n
copies of the 2Dposition oof the optical axis in the image plane. For faces at a
distance exceeding 10×the radius of a subject’s head we can use an affine projection
with Pi=Pj,i, jinstead, without incurring any significant approximation errors.
The 2D mesh of projected vertices, ˜
s, samples the input 2D image. Stacking the
RGB values of the corresponding samples into a vector aI
aI= (rI
1, gI
1, bI
1, ......., rI
n, gI
n, bI
we can then compare the synthesised and input images by measuring the error ||aI
aM||. Noting that the samples picked from the input image by the mesh are a function
of ρand α, the objective of the fitting process is to solve the optimisation problem
α,β,ρ,µ kaI(ρ, α)aM(ρ, α,β, µ)k2+λ1kα÷σsk2+λ2kβ÷σtk2(17)
where the last two terms induce regularisation of the estimated parameters. The symbol
÷denotes element-wise division.
The problem formulated in (17) is very challenging because of its nonlinearity and
its ill-posed nature [14, 15]. The conventional approach to optimisation is to apply
the Newton Optimisation algorithm involving the sampling of random subsets of mesh
vertices to achieve computational feasibility [2, 5]. These challenges motivated the
developments reviewed in Section 2 but any speed-up therein is only achieved at the
expense of restricted applicability.
In the following section we propose a novel method of fitting 3DMM that is more
than an order of magnitude faster than the existing algorithms, without imposing any
restrictions on the camera model and lighting. The computational efficiency is achieved
by breaking the fitting problem up to create a sequence of optimisation tasks, most
of which are linearised to render closed form solutions. The proposed strategy has
the additional major benefit for illumination and albedo estimation of simultaneously
involving all the model vertices in optimisation. This avoids local optima and leads to
more accurate fitting.
4. Efficient Stepwise Optimisation (ESO)
This section describes our ESO framework. ESO is a SeqOpt method which groups
all the parameters into 5 categories: pose with camera parameters, shape, light direc-
tion, light strength and albedo. The parameters in each group are optimised under the
geometric refinement photometric refinement
Figure 3: The ESO fitting process topology. Each of the two main phases of the fitting process - geometric
and photometric - are iterated until convergence is achieved.
assumption that those in all the other groups are known, or have no impact on the opti-
misation process. The parameter grouping strategy aids the linearisation of the 3D face
model fitting process, but further group-specific linearisation measures are adopted, as
required. These are detailed in the respective sections.
The proposed method divides the fitting process into two phases, namely geometric
and photometric optimisation as shown in Fig. 3. The geometric phase aligns an input
image to a 3DMM, and the photometric phase recovers its reflectance. Each phase
consists of three stages that are iterated in turn a few times to refine the solution. A key
contribution of our approach is the proposed linearisation of all but one stage of the op-
timisation process that leads to closed-form solutions, and consequently computational
efficiency. In Sections 4.1 to 4.6, each step of ESO is explained in more detail.
4.1. Camera Parameter Estimation
The first step uses the input image facial landmarks to estimate the subject’s pose
and the camera parameters that roughly align the input image to the model. Let us
consider an identifiable point e
i= (˜xI
i)Ton the face of the input image, which
semantically corresponds to the ith vertex of the 3D face model with coordinates wi=
(xi, yi, zi)T. Image landmarks typically include the locations of the eye and mouth
Figure 4: Visualisation of the facial landmarks used throughout this paper
corners, tip of the nose etc.2In this work, a maximum of 28 landmarks are used as
shown in Fig. 4. However, some of these landmarks are not visible for non-frontal
poses due to self-occlusion. In those cases, only the visible landmarks are used. Also,
in the first iteration, the contour landmarks (7 shown in Fig 4) are not available.
For the alignment, we need to find the rigid transformation R,τthat moves the
coordinates of the point wi= (xi, yi, zi)Tto its new position w0
i= (x0
i, y0
i, z0
i)Tso that
after wiis projected to 2D via the mapping W(ρ), its 2D coordinates e
wi= (˜xi,˜yi)T
are as close as possible to the image point e
i= (˜xI
Let Ldenote the subset of vertices corresponding to the facial landmark points in
the input image. Then the pose and camera parameters ρ={R,τ, f, o}can be esti-
mated by minimising the distance between the input landmarks and those reconstructed
from the model:
This is the only cost function which is not linearised. It is minimised by the Levenberg-
Marquardt algorithm [7], but because of the small number of parameters involved, the
convergence is fast. Note that e
widepends on both the pose and camera parameters, as
well as the shape model s. The latter is kept constant in this step, and in the first itera-
tion, sis set to s0. In subsequent iterations, sis replaced by the shape update obtained
2Landmark detection itself is outside the scope of this paper.
in the previous iteration by the second stage of the fitting process described in the next
subsection. The estimated pose and camera parameters feed into the shape estima-
tion stage described in Section 4.2. The contour landmarks described in Section 4.3
constrain the pose and camera parameters, and shape estimation.
4.2. Shape Parameters Estimation
Once the pose and camera parameters are recovered, the shape parameters αcan
be estimated. We linearise this problem by making use of the current estimate of the
model vertex coordinates zi,ito define the projection matrices Pi. In addition, in
contrast to prior art, we define the cost function in 3D space, as:
where the image landmarks e
i,i∈ L, are back-projected to wI
i= (xI
i, yI
i, zI
i, ρ). The main motivation for working in 3D is to reduce computational
complexity further.
Since wiis a vertex of the shape model s, it is a function of α. The cost function is
defined in 3D as:
where: ˆ
sIand ˆ
sare the stacked vertex positions wI
iand wi,i∈ L, respectively;
s0and ˆ
Sare constructed by choosing those elements from s0and S
(defined in Eq. (2)) corresponding to the landmark indices L;λ1is a free weighting
parameter; αTσ1
sαis a regularisation term based on Eq. (3).
The closed-form solution for αin is:
where σsis defined in Section 3.
Finally, we explain how to implement the inverse projection W1. Note that e
cannot be back-projected to wI
iin the face model coordinate system unless zI
i, the
depth along the zaxis of wI
i, is known. Here, in the first iteration, zI
iis approximated
(a) model image (b) contour edge (c) image landmarks (d) correspondence
Figure 5: Contour landmarks detection. Yellow and red dots represent the contour landmarks of the input
and model reconstructed images, respectively. Algorithm 1 bridges (c) and (d).
by the model vertex zi, which is constructed from the mean shape s0. As the face shape
is updated in subsequent iterations, the latest estimate sis used in place of s0.
4.3. Contour Landmark Constraints
One impediment to accurate 3D shape reconstruction from a non-frontal 2D face
image stems from the lack of constraints on the projection of the occluding face con-
tour. In [7], the authors define the contour edges as the occluding boundary between
the face and non-face area, and use them to constrain the fitting. The contour edges
of the 2D face image synthesised from a 3D model-based reconstruction of a 2D input
image are shown in Fig. 5b. They are formed by linking the 3D model vertices lying
on the occluding boundary of the projected 3D face mesh, determined by the vertex
visibility check. A recent review of techniques developed to fit 3DMM to edges can
be found in [24]. To reduce the computational cost of working with contour edges, we
only use contour landmarks lying on the contour boundary. Here a contour landmark is
defined as the point of intersection of the occluding boundary of a face and a horizontal
line in the face coordinate system passing the corresponding symmetric landmark, as
shown in Fig. 6. Such contour landmarks are labelled in the input image automatically
by a cascaded-regression-based algorithm for automatic facial landmark detection [25],
which has been trained to detect contour landmarks defined in this way.
The vertices Lcthat form the contour landmarks along the occluding boundary
of the fitted 3DMM are found using Algorithm 1. They are the vertices (red dots in
Fig. 5d) closest to the contour landmarks of the input image (yellow dots of Fig. 5c).
Once this correspondence is established, these contour landmark pairs are added to the
available landmark set Lin Eq. (18) and (20) to improve the estimation of camera
parameters and shape.
Figure 6: Definition of the contour landmarks. The axis of face symmetry is defined by the chin and the
centre of the nose bridge. The face contour landmarks are the points of intersection of (i) the input image
face occluding contour and (ii) the horizontal lines in the thus-defined face coordinate system, passing the
visible facial contour landmarks.
4.4. Light Direction Estimation
After geometric fitting (Section 4.1-4.3), the 3DMM model is aligned to the input
image, and the reflectance parameters can be estimated. In this step, we focus on the
light direction d, and regard all other variables as constant. Recalling Eq. (6), the cost
function can be formulated as:
The minimisation of Eq. (22) is a non-linear problem because of the exponential
form of ein Eq. (10). To eliminate this nonlinear dependence we precompute the value
of ebased on the assumptions that: i)ksand γare constant; ii)the values of vand
rare set to those of the previous iteration. In order to make the linearity of the light
direction fitting problem more transparent, we avoid the element-wise multiplication
2D contour landmarks coordinates η={η1...ηk1}output by [25]
3DMM rendered contour edge coordinates ζ={ζ1...ζk2}(k2k1) via W
3D vertex indices φ={φ1...φk2}corresponding to ζ
Output: 3D vertex indices Lccorresponding to η
1for i= 1; ik1;i+ + do
2for j= 1; jk2;j+ + do
i=φarg minj{distj}
7return Lc
Algorithm 1: Establishing the contour landmark correspondence
in ( 22) by reformulating the cost function as:
where A= [ˇ
ldt]R3n×3. By this reformulation, a closed-form solution
can be found as: d= ((AN3)T(AN3))1(AN3)T(aIˇ
lde). Then d
is normalised to a unit vector.
For the first iteration, we initialise the values of t,ˇ
laand ˇ
ldas follows. 1) In
common with [26, 27], we assume that the face is a Lambertian surface. Consequently,
only the diffuse light in Eq. (6) is modelled. 2) The strengths of diffuse light ˇ
albedo tare set to vectors whose entries are all 1 and t0respectively. With these
assumptions, the cost function in the first iteration becomes:
where B= [t0,t0,t0]R3n×3. The closed-form solution is: d= ((BN3)T(B
The estimated light direction is fed into the light strength and albedo estimations
detailed in Section 4.5 and Section 4.6.
4.5. Light Strength Estimation
Having obtained an estimate of d, the ambient and directed light strengths can
be recovered. Because the three colour channels can be processed independently, for
simplicity only the red channel is described. The cost function for the red channel is:
kaI,r Cˇ
where aI,r is the red channel of aI;C= [tr,tr(Nd) + er]Rn×2,trand erare the
red channels of tand e;lr
ad = (lr
a, lr
d)T, where lr
aand lr
dare the strengths of ambient
and directed lights of the red channel respectively. The closed-form solution for lr
ad is:
ad = (CTC)1CTaI,r (26)
Note that tis set as stated earlier in Section 4.4. The green and blue channels are solved
in the same way.
4.6. Albedo Estimation
Once the light direction and strengths are recovered, the albedo can be estimated.
To avoid over-fitting, we regularise the albedo estimation and generate the cost func-
where λ2is a free weighting parameter. The closed-form solution is
in t0)(28)
where σtis as defined in Section 3 and aI
in, the illumination-normalised image, is given
in = (aIˇ
ld(N3d)) (29)
where the symbol ÷denotes element-wise division as before.
4.7. Computational complexity
The computational complexity of our method is dominated by the albedo estima-
tion described above. From Eq. (28) we can see that, since nrt, the dominating
computations are the two matrix multiplications, both of which have a complexity of
O(n rt
5. Experiments
In this section, a comprehensive evaluation of our methodology is described. First,
face reconstruction performance is evaluated. Then, in face recognition experiments,
we compare our ESO with the existing 3DMM methods and other state-of-the-art meth-
ods. We implemented two effective 3DMM fitting methods [7] and [10], and the free
parameter settings of [7, 10] follow the original papers. The results of all the other
methods are cited from their papers based on the same experimental settings.
5.1. Face Reconstruction
First, we present some qualitative fitting results in Fig. 7. These images are from
the Multi-PIE database. The people in these images have different gender, ethnicity and
facial features such as a beard and/or glasses. All these factors can cause difficulties
for fitting. As can be seen in Fig. 7, the input images are well fitted. Note that our
3DMM does not model glasses. Therefore, the glasses of an input image, such as the
3rd person in Fig. 7, can confuse the fitting process. Despite it, our ESO reconstructs
this face well, showing its robustness.
In order to quantitatively measure every component of ESO, the 2D input images
and their corresponding ground truths of camera parameters, 3D shape, light direction
and strength, and texture need to be known. To meet all these requirements, we gen-
erated a local database of rendered 2D images with all the 3D ground truth as follows:
(1) We collected and registered 20 3D face scans. The first 10 scans are used for model
selection, and the remaining scans are used for performance evaluation. (2) The regis-
tered 3D scans are projected to PCA space, parameterising the ground truth in terms of
coefficients αand β. (3) Using the registered 3D scans, we rendered 2D images under
Figure 7: Row 1: input images with different pose and illumination variations. Row 2: ESO-
fitted/reconstructed images.
different poses and illuminations. (4) The 3DMM is fitted to obtain estimates of all
these parameters. (5) Reconstruction performance is measured using cosine similarity
between the estimated and ground-truth αor β.
5.1.1. Effects of Hyperparameters
Before we evaluate the face reconstruction performance, the sensitivity of the hy-
perparameters of ESO on the fitting process is investigated. The relevant hyperparam-
eters are the regularisation weights λ1in Eq. (20) and λ2in Eq. (27) and the number of
iterations (l1and l2) for geometric and photometric refinements (Fig. 3), respectively.
All the renderings in Section 5.1.1 are generated by setting both the focal length and
the distance between the object and camera to 1800 pixels as suggested in [28].
Impact of the weight λ1on shape reconstruction The weight λ1should be selected
carefully because improper λ1will cause under- or over-fitting during shape recon-
struction. As shown in Fig. 8, the reconstruction using a large λ1(= 1000) looks very
smooth and the shape details are lost, exhibiting typical characteristics of under-fitting.
On the other hand, a small λ1(= 0) causes over-fitting, and the reconstruction in Fig. 8
is excessively stretched. In comparison, the reconstruction with λ1= 0.5recovers the
shape well.
To quantitatively evaluate the impact of λ1, 2D renderings under 3 poses (frontal,
side and profile), without directed light, are generated. To decouple the impact of λ1
Figure 8: Impact of λ1and λ2on shape and albedo reconstruction. Column 1: input image, Column 2:
ground truth of shape and albedo, Column 3-5: reconstructions with different λ1and λ2.
and l1on shape refinement, l1is set to 1. As shown in Fig. 9a, neither small ( <0.4)
nor large (>1) λ1lead to good reconstruction which is consistent with Fig. 8. On the
other hand, the reconstructions of all 3 poses do not change much with λ1in the region
between 0.4 and 0.7. Hence, λ1is set to 0.5, which is the average value of the best λ1
over all the test cases, to simplify parameter tuning.
Impact of the number of iterations l1on shape refinement The same renderings
are also used to evaluate the sensitivity to l1. From Fig. 9b, we can see that more
than 3 iterations do not greatly improve the reconstruction performance for any pose.
Therefore, l1is fixed at 3 in the remaining experiments.
Impact of the weight λ2on albedo reconstruction We also examine the impact of
λ2on albedo reconstruction. Fig. 8 shows some qualitative results. Clearly, the re-
construction with λ2= 1000 loses the facial details because of being under-fitted. On
the other hand, the one with λ2= 0 does not separate the illumination and albedo
properly, causing over-fitting. In comparison, the one with λ2= 0.7reconstructs the
albedo well.
To quantitatively investigate the impact of λ2on the estimated light direction and
strength, the renderings from different light direction dand strength ld3are used as
shown in Fig. 9c. All these renderings are under frontal pose and l2=1. It is clear that
the reconstructed albedo does not change greatly with λ2in the region between 0.2 and
1. To simplify parameter tuning, λ2is fixed to 0.7 which is the average value of the
best λ2over all the test cases.
Impact of the number of iterations l2on albedo refinement To investigate the impact
of l2, the same 2D renderings for the λ2evaluation are used. As shown in Fig. 9d, all
the images converge by the 4th iteration. Hence, for simplicity, l2is fixed to 4 in ESO.
5.1.2. Reconstruction Results
We evaluate shape and albedo reconstructions separately. ESO is compared with
two methods: MFF [7] and [10], which are the best SimOpt and SeqOpt methods,
respectively. We implemented the whole framework of MFF. Regarding [10], we
only implemented the geometric (camera model and shape) part, because insufficient
implementation details of the photometric part were released.
Shape Reconstruction As mentioned in Section 1 and 2, the affine camera used by
[10] cannot model perspective effects, while the perspective camera used by ESO and
MFF can. Different camera models lead to different shape reconstruction strategies. In
order to find out how significant this difference is, we change the distance between the
object and camera to generate perspective effects, at the same time keeping the facial
image size constant by adjusting the focal length to match [28]. Note that the shorter
this distance, the larger the perspective distortion. To compare shape reconstruction
performance, 2D renderings under frontal pose obtained for 6 different distances are
generated. We can see from Fig. 10 that the performance of ESO and MFF remains
constant under different perspective distortions. However, the performance of [10]
reduces greatly as the distance between the object and camera decreases. Also, ESO
consistently works better than MFF under all perspective distortions.
Albedo Reconstruction We compare ESO with MFF [7] in Table 1 using images ren-
dered under different light direction and strength. We see that the albedo reconstruction
3The illumination is set to be white here, i.e. ld= (ld, ld, ld)T
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 5 10 100 1000
cosine similarity of shape
\ \ \ \
(a) Impact of regularisation weight λ1on shape
reconstruction over poses
cosine similarity of shape
(b) Impact of the number of iterations l1on
shape reconstruction
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 5 10 100 1000
cosine similarity of albedo
left−light, ld = 0.5
right−light, ld = 0.5
frontal−light, ld = 0.5
frontal−light, ld = 0.1
frontal−light, ld = 1
\ \ \ \
(c) Impact of regularisation weight λ2on
albedo reconstruction over lightings
cosine similarity of albedo
left−light, ld = 0.5
right−light, ld = 0.5
frontal−light, ld = 0.5
frontal−light, ld = 0.1
frontal−light, ld = 1
(d) Impact of the number of iterations l2on
albedo reconstruction
Figure 9: Effects of hyperparameters on facial shape and albedo reconstruction
performance for different light direction is very similar, but it varies greatly for differ-
ent directed light strength. This demonstrates that the albedo reconstruction is more
sensitive to light strength than direction. Also, ESO consistently works better than
MFF. The reasons are two fold: 1) MFF uses a gradient-based method that suffers
from the non-convexity of the cost function. 2) For computational efficiency, MFF ran-
domly samples only a small number (1000) of polygons to establish the cost function.
This is insufficient to capture the information of the whole face, causing under-fitting.
Our method being much faster makes use of all the polygons. Further computational
0 500 1000 1500 2000
distance between the camera and object (unit: pixel)
cosine similarity of shape
[8] with affine camera
ESO with perspective camera
MFF with perspective camera
Figure 10: Shape reconstruction results measured by cosine similarity
Table 1: Albedo reconstruction results measured by cosine similarity.
light direction light strength ldMFF [7] ESO
left 0.5 0.57 ±0.15 0.61 ±0.08
right 0.5 0.57 ±0.13 0.60 ±0.08
frontal 0.5 0.58 ±0.14 0.62 ±0.08
frontal 0.1 0.60 ±0.13 0.67 ±0.07
frontal 1.0 0.49 ±0.16 0.54 ±0.08
efficiency discussions can be found in Section 5.2.1.
5.2. Pose and Illumination Invariant Face Recognition
Pose- and illumination-invariant face recognition is a challenging problem addressed
by a variety of approaches [29]. 2D methods address the pose and illumination prob-
lem at either pixel-level or image feature-level. The former aim to create pixel-level
correspondence across different poses [30, 31, 32, 33]. For example, regression-based
methods [31, 32] learn mapping matrices which project images of one particular pose
to another one. The latter project the pixel values into pose- and/or illumination-robust
feature spaces[11, 34, 35, 36, 37]. For example, Canonical Correlation Analysis [34]
projects pixel values to a subspace where the impacts of pose and illumination are
effectively removed. Deep Learning [11, 36] is also based on the same motivation.
3D methods intrinsically model pose variations using the analysis-by-synthesis ap-
proach. This means that a 3D face model has to be fitted to the input 2D image an-
notated with facial landmarks. The methods can be categorised into 3 groups: pose
normalisation [18, 38, 39], pose synthesis [40, 41] and 3D shape and texture feature
extraction [2, 7, 42]. Pose normalisation renders all the images (gallery and probe) to
a frontal view using 3D models; pose synthesis renders multiple gallery images of dif-
ferent poses for each subject. A probe image is only matched against that of the most
similar pose in the gallery. 3D shape and texture feature extraction methods attempt
to match probe and gallery images in a 3D parameter space, providing a pose- and
illumination-invariant representation.
Among other options, 3DMM-based face recognition systems [7, 8, 9, 10] have a
particular appeal because the process of 3D face model fitting provides a means of ex-
tracting the intrinsic 3D face shape and albedo from an unconstrained input face image.
However, for a long time, the wide spread use of 3DMM in face recognition has been
inhibited by inefficient 3D face model fitting algorithms. The ESO fitting algorithm
presented in Section 4 offers a new means that greatly enhances the applicability of the
3D face model fitting approach.
Most existing 3DMM methods [7, 8, 9, 10] assume that accurate facial landmarks
are known. To the best of our knowledge, only one previous work [43] proposes the use
of automatically detected landmarks. In [43], the automatic landmark detection and
3DMM fitting are combined by a data-driven Markov chain Monte Carlo method. This
method is robust to automatically detected landmarks but is rather slow. In contrast, we
use an efficient cascaded regression technique [25] to automatically detect landmarks,
which are then fed into a fully automatic face recognition system.
The conventional pipeline of a 3DMM face recognition system, shown as Scheme
1 in Fig. 11, involves the use of the generative 3D shape and texture parameters (α
and β), which are isolated from the input image appearance by suppressing the pose
and illumination nuisance parameters. As in previous works [7, 9, 10], αand βare
concatenated into a single vector to work as a holistic descriptor.
The drawback of holistic features is their inability to capture local facial proper-
ties, e.g. a scar, which may be very discriminative between people. To overcome this
problem, we propose to extract local features as an alternative. Specifically, with the as-
sistance of ESO fitting, we can render a pose- and illumination-normalised face image
Scheme 2
Local Feature
Holistic Feature
[󰑚, 󰓏]
Figure 11: Face recognition pipeline. Scheme 1 and 2 use holistic and local features for face recognition
from an unconstrained input face as shown in Scheme 2 in Fig. 11. The pose normali-
sation is achieved by setting ρ=ρ0to transform the input face to a canonical frontal
view. The illumination-normalised input image aI
in is obtained using Equation 29. Lo-
cal features, such as those from the Local Phase Quantisation (LPQ) [44] descriptor
used in this work, can then be extracted from this rendered image.
We evaluate the merit of the ESO fitting approach in the context of face recognition
on the PIE [45] and Multi-PIE [46] databases which both have large pose and illumi-
nation variations. We set the hyperparameters {λ1, l1, λ2, l2}of ESO to {0.5, 3, 0.7,
4}as discussed in Section 5.1.
5.2.1. PIE Database
PIE is a benchmark database that can be used to compare different 3DMM fitting
Protocol To compare all the methods fairly, the standard experimental protocol is used
by our system. In particular, the recognition performance is measured using a subset
of PIE including 3 poses (frontal, side and profile) and 24 illuminations. In order to
conform to the protocol, in this experiment the fitting is initialised by manual land-
marks. The gallery set contains frontal face images under neutral illumination, and the
remaining images are probes. The holistic features α,βare used to represent a face.
Results Face recognition performance in the presence of combined pose and illumina-
Table 2: Face recognition rate (%) on different poses averaging over all the illuminations on PIE
frontal side profile average
LiST [8] 97 91 60 82.6
Zhang [9] 96.5 94.6 78.7 89.9
Aldrian [10] 99.5 95.1 70.4 88.3
MFF [7] 98.9 96.1 75.7 90.2
ESO 100 97.4 73.9 90.4
tion variations is reported in Table 2. ESO performs substantially better than [8], and
marginally better than [7, 9, 10]. Note that MFF [7], whose performance is very close
to ESO, has more than 10 hyperparameters, causing difficulties for optimal parameter
selection. In contrast, ESO has only 4 hyperparameters.
Runtime The optimisation time was measured on a computer with Intel Core2 Duo
E8400 CPU and 4GB RAM memory. The results obtained for our implementation of
the SimOpt method (MFF [7]) and the results reported for the SeqOpt method [10]
are compared with those obtained with ESO. MFF took 23.1 seconds to fit one image,
while ESO took only 2.1 seconds on average per fitting. The authors of [10] did not
report their run time, but they also determined the albedo estimation to be the dominant
step, with the same complexity of O(n rt
2). Note however that [10] uses not only one
group of global αand βbut also four additional local groups to represent a face, while
we only use the global parameters. Therefore rtin our approach is one fifth of [10],
giving a 25-fold speed advantage.
5.2.2. Multi-PIE Database
To compare with other state-of-the-art methods, evaluations are also conducted on
a larger database, Multi-PIE, containing more than 750,000 images of 337 people. In
addition, our face recognition systems, initialised by both manually and automatically
detected landmarks, are compared. We used a cascaded regression-based automatic
landmark detection method [25].
Protocol There are two settings, Setting-I and Setting-II, widely used in previous work
[11, 12, 36, 38]. Setting-I is used for face recognition in the presence of combined pose
Table 3: Face recognition rate (%) on different poses averaging all the illuminations on Multi-PIE (Setting-I)
Method Annotation Feature -45-30-15+15+30+45Mean 0
Li [31] Manual Gabor 63.5 69.3 79.7 75.6 71.6 54.6 69.1 N/A
Learning Automatic
RL [11] 66.1 78.9 91.4 90.0 82.5 62.0 78.5 94.3
FIP [11] 63.6 77.5 90.5 89.8 80.0 59.5 76.81 94.3
MVP [12] 75.2 83.4 93.3 92.2 83.9 70.6 83.1 95.7
Automatic Holistic 73.8 87.5 95.0 95.1 90.0 76.2 86.3 98.7
Local 79.6 91.6 98.2 97.9 92.6 81.3 90.2 99.4
Manual Holistic 80.8 88.9 96.7 97.6 93.3 81.1 89.7 99.1
Local 81.1 93.3 97.7 98.0 93.3 82.4 91.0 99.6
Table 4: Face recognition rate (%) on different poses under neutral illumination on Multi-PIE (Setting-II)
Method Annotation -45-30-15+15+30+45Mean
PLS [32]
51.1 76.9 88.3 88.3 78.5 56.5 73.3
CCA [47] 53.3 74.2 90.0 90.0 85.5 48.2 73.5
GMA [48] 75.0 74.5 82.7 92.6 87.5 65.2 79.6
DAE [49] Automatic 69.9 81.2 91.0 91.9 86.5 74.3 82.5
SPAE [36] 84.9 92.6 96.3 95.7 94.3 84.4 91.4
Asthana [38]
74.1 91.0 95.7 95.7 89.5 74.8 86.8
MDF [50] 78.7 94.0 99.0 98.7 92.2 81.8 90.7
ESO+LPQ 91.7 95.3 96.3 96.7 95.3 90.3 94.4
and illumination variations, Setting-II for that with only pose variations.
In common with [11, 12], Setting-I uses a subset in session 01 consisting of 249
subjects with 7 poses and 20 illumination variations. The images of the first 100 sub-
jects constitute the training set. The remaining 149 subjects form the test set. In the test
set, the frontal images under neutral illumination work as the gallery and the remaining
are probe images. Following [36, 38], Setting-II uses the images of all the 4 sessions
(01-04) under 7 poses and only neutral illumination. The images from the first 200
subjects are used for training and the remaining 137 subjects for testing. In the test set,
the frontal images from session 01 work as gallery, and the others are probes.
ESO vs Deep Learning (Setting-I) In recent years, deep learning methods have achieved
considerable success in a range of vision applications. In particular, deep learning
works well for pose- and illumination-invariant face recognition [11, 12]. To our
knowledge, these methods have reported the best face recognition rate so far on Multi-
PIE over both pose and illumination variations. Systems deploying these methods
learned 3 pose- and illumination-invariant features: FIP (face identity-preserving), RL
(FIP reconstructed features), and MVP (multi-view perceptron) using convolutional
neural networks (CNN). Table 3 compares ESO with these deep learning methods and
the baseline method [31]. Not surprisingly, deep learning methods work better than
[31] because of their powerful feature learning capability. However, ESO with auto-
matic annotation, using either holistic or local features, outperforms these three deep
learning solutions as shown in Table 3. We conclude that the superior performance
of ESO results from the fact that the fitting process of ESO can explicitly model the
pose. In contrast, the deep learning methods try to learn the view/pose-invariant fea-
tures across different poses. This learning objective is highly non-linear so that the
methods tend to get trapped in local minima. In contrast, ESO solves several convex
problems and avoids this pitfall.
Automatic vs Manual Annotation (Setting-I) Table 3 also compares the performance
of ESO with fully automatic annotation against that based on manual annotation. This
table shows that the mean face recognition rates of the fully automatic system are close
to those relying on manual annotation: 88.0% vs 91.2% for holistic features, and 91.5%
vs 92.2% for local features. It means that ESO is reasonably robust to the errors caused
by automatically detected landmarks.The superiority of local features, which can cap-
ture more facial details than holistic features, is also evident from the results.
ESO for Pose-robust Face Recognition (Setting-II) Table 4 compares ESO with the
state-of-the-art methods for pose-robust face recognition. The methods can be clas-
sified into 2D and 3D approaches as discussed in Section 5.2. In the 2D category,
PLS [32] and CCA [47] are unsupervised methods, and consequently they deliver in-
ferior performance. GMA [48] benefits from its use of some additional supervisory
information. DAE [49] and SPAE [36] are auto-encoder-based methods, which have
superior capability to learn the non-linear relationships between images of different
poses. SPAE set the state-of-the-art in performance, even compared with 3D methods
[38] and [50]. However, our ESO outperforms SPAE, specifically 94.4% vs 91.4%,
because of its accurate shape and albedo reconstruction capability.
6. Conclusions
We proposed a new optimisation method — Efficient Stepwise Optimisation (ESO)
— for fitting a 3D morphable face model to a 2D face image. In order to improve the
optimisation efficiency, the method decouples the geometric and photometric optimi-
sations and uses least squares sequentially to optimise the reconstructed shape, light di-
rection, light strength and albedo parameters in separate steps. It includes a perspective
camera model that becomes important in view of the growing interest in near-camera
The computational efficiency of ESO is achieved thanks to the proposed lineari-
sation of the model fitting steps, leading to closed-form solutions. ESO improves the
optimisation efficiency by an order of magnitude in comparison with [7]. Moreover, it
overcomes the weaknesses of earlier SeqOpt methods:
The shape reconstruction of ESO supports a perspective camera.
ESO linearises the Phong model.
It models specularity.
Occluding contour landmarks (Section 4.3) are used for a more robust fitting.
The experimental results demonstrate that the face reconstruction achievable by ESO
is an improvement on that obtained from the state-of-the-art methods.
The ESO fitting algorithm can extract both holistic features and local features. A
face recognition system that incorporates ESO to facilitate pose and illumination in-
variance was constructed, and evaluated on the PIE and Multi-Pie benchmark datasets
with very promising results.
7. Acknowledgments
Support for this work is gratefully acknowledged from: EPSRC/DSTL project
EP/K014307/1 “Signal processing in a networked battlespace”; EPSRC Programme
Grant EP/L000539 “S3A: Future spatial audio for immersive listener experiences at
home”; and the European Commission FP7 project 284989 “BEAT”.
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