Claudia Arellano

Adolfo Ibáñez University

The LivDet‐2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genuine) images used to train such algorithms. Unfortunately, th...

Biometric has been increasing in relevance these days since it can be used for several applications such as access control for instance. Unfortunately, with the increased deployment of biometric applications, we observe an increase of attacks. Therefore, algorithms to detect such attacks (Presentation Attack Detection (PAD)) have been increasing in...

Semantic segmentation has been widely used for several applications, including the detection of eye structures. This is used in tasks such as eye-tracking and gaze estimation, which are useful techniques for human-computer interfaces, salience detection, and Virtual reality (VR), amongst others. Most of the state of the art techniques achieve high...

Selfie soft biometrics has great potential for various applications ranging from marketing, security, and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a super-resolution convolutional neural networks (SRCNNs) approach that increases the resolution of low-quali...

An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregula...

Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris seg-ment...

Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris segmenta...

Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low qualit...

Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low qualit...

The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is repeated several times to detect multiple...

This paper proposes to infer accurately a 3D shape of an object captured by a depth camera from multiple view points. The Generalised Relaxed Radon Transform (GR2T) [1] is used here to merge all depth images in a robust kernel density estimate that models the surface of an object in the 3D space. The kernel is tailored to capture the uncertainty as...

We propose to fit automatically a 3D morphable face model to a point cloud captured with a RGB-D sensor. Both data sets, the shape model and the target point cloud are modelled as two probability density functions (pdfs). Rigid registration (rotation and translation) and reconstruction on the model is performed by minimising the Euclidean distance...

We present a Mean Shift algorithm for fitting shape models. This algorithm maximises a posterior density function where the likelihood is defined as the Euclidean distance between two Gaussian mixture density functions, one modelling the observations while the other corresponds to the shape model. We explore the role of the covariance matrix in the...

3D reconstruction from multiple view images requires that camera parameters are very accurately known and standard camera calibration techniques [1] often fail to provide the required level of accuracy for the extrinsic camera parameters. Using the Kinect depth camera, we propose to estimate camera parameters by minimising the cross correlation bet...

In this paper, we present a Mean Shift algorithm that does not require point correspondence to fit shape models. The observed data and the shape model are represented as mixtures of Gaussians. Using a Bayesian framework, we propose to model the likelihood using the Euclidean distance between the two Gaussian mixture density functions while the late...

We present a Mean shift (MS) algorithm for solving the rigid point set transformation estimation [1]. Our registration algorithm minimises exactly the Euclidean distance between Gaussian Mixture Models (GMMs). We show experimentally that our algorithm is more robust than previous implementations [1], thanks to both using an annealing framework (to...

Incluye resumen en español. Tesis (Magíster en Ciencias de la Ingeniería)--Pontificia Universidad Católica de Chile, 2005. Incluye bibliografía.