Conference Paper

Robust sparse coding for face recognition

Hong Kong Polytech. Univ., Hong Kong, China
DOI: 10.1109/CVPR.2011.5995393 Conference: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Source: IEEE Xplore

ABSTRACT

Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity-constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.

  • Source
    • "The section seeks to assess the effectiveness of the proposed GCDL method compare to state of the art in sparse classification. Several SRC families 1 and dictionary learning methods are used for comparison that are developed recently, SRC (Wright et al., 2009), RSC (Yang et al., 2011b), ESRC (Deng et al., 2012), and RADL (Wei and Wang, 2015). Baseline considered methods in the literature can be categorized as methods without external dictionary (SRC and RSC) with external dictionary (such as ESRC and RADL wo ) and with both dictionary learning and classification like (SVDL and RADL w DL). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured in operational environments, and over multiple cameras, to improve robustness to variations. This paper focuses on Sparse Representation Classification (SRC) techniques that are suitable for the design of still-to-video FR systems based on under-sampled dictionaries. The limited reference data available during enrolment is complemented by an over-complete external dictionary that is formed with an abundance of faces from non-target individuals. In this paper, the Graph-Compressed Dictionary Learning (GCDL) technique is proposed to learn compact auxiliary dictionaries for SRC. GCDL is based on matrix factorization, and allows to maintain a high level of accuracy with compressed dictionaries because it exploits structural information to represent intra-class variations. Graph factorization compression has been shown to efficiently compress data, and can therefore rapidly construct compressed dictionaries. Accuracy and efficiency of the proposed technique is assessed and compared to reference sparse coding and dictionary learning technique using videos from the CAS-PEAL database. GCDL is shown to provide fast matching and adaptation of compressed dictionaries to new reference faces from the video surveillance environments.
    Full-text · Conference Paper · Feb 2016
  • Source
    • "Experimental results show that LPDP is more suitable for recognition tasks than LPP. Sparse representation, as a new branch of the state-of-theart techniques for signal representation, has attracted considerable research interests272829303132333435363738. It attempts to preserve the sparse representation structure of the samples in a lowdimensional embedding subspace. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Dimensionality reduction is extremely important for understanding the intrinsic structure hidden in high-dimensional data. In recent years, sparse representation models have been widely used in dimensionality reduction. In this paper, a novel supervised learning method, called Sparsity Preserving Discriminant Projections (SPDP), is proposed. SPDP, which attempts to preserve the sparse representation structure of the data and maximize the between-class separability simultaneously, can be regarded as a combiner of manifold learning and sparse representation. Specifically, SPDP first creates a concatenated dictionary by classwise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least square method. Secondly, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds. Then, SPDP integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experimental results on several popular face databases demonstrate the feasibility and effectiveness of the proposed approach.
    Full-text · Article · Jan 2016 · Mathematical Problems in Engineering
  • Source
    • "The SRC method classifies the test sample by evaluating which class of training samples has the minimum reconstruction error with the associated coding coefficients. This method is selected because of its high performance reported in former studies [12], in addition to being real-time. The SRC is applied on the detected faces by the well-known Viola-Jones [13] method. "
    [Show description] [Hide description]
    DESCRIPTION: One of the main challenges of current person recognition techniques lies on difficulties of recognition in various poses. Recently, attention has been focused on using soft biometric information extracted from the human body to overcome the biometric recognition system’s limitation in unconstrained environments. In this paper, we integrate the face and body information in a linear combination. We propose a novel approach in which the weights of features in the recognition system are adapted based on the reliability of the detected joints extracted from the body and the correlation between features. We evaluate the proposed approach in recognizing a five person group in various poses such as sitting and circular walking. The method was applied to a service robot equipped with the Kinect sensor. The results show a mean improvement of 4.39% after weight adaptation based on the correlation between features and 6.88% after consideration of the reliability of the features.
    Full-text · Research · Jan 2016
Show more