3D Face Recognition with Sparse Spherical Representations

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a novel and fully automated registration process. They are then represented as signals on the 2D sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where Linear Discriminant Analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images

Pattern Detection by Distributed Feature Extraction

This paper presents a distributed algorithm for the detection of patterns or their transformed versions, in noisy images. The proposed method projects the observed signal onto a redundant and structured dictionary of functions, which are distributed among general purpose vision sensors. Each of the sensors then approximates the projections on its own part of the dictionary, and transmits that short information to a central fusion center. The pattern detection problem is then cast to a parameter estimation problem, where the parameters of the geometric transformation of the pattern of interest are sought, instead of the pattern itself. The parameters of the transformation are estimated by introducing a score function over the parameter space. Such an approach allows the fusion center to directly work in the space of features computed by the sensors, without need for signal reconstruction. It advantageously provides a generic approach, where the processing of the image is directly driven by the detection task. Experimental results indicate the effectiveness of the proposed method and its resiliency to noise in the observation

Classification-Specific Feature Sampling for Face Recognition

Feature extraction based on different types of signal filters has received a lot of attention in the context of face recognition. It generally results into extremely high dimensional feature vectors, and sampling of the coefficients is required to reduce their dimensionality. Unfortunately, uniform sampling that is commonly used to that aim, does not consider the specificities of the recognition task in selecting the most relevant features. In this paper, we propose to formulate the sampling problem as a supervised feature selection problem where features are carefully selected according to a well defined discrimination criterion. The sampling process becomes specific to the classification task, and further facilitates the face recognition operations. We propose to build features on random filters, and Gabor wavelets, since they present interesting characteristics in terms of discrimination, due to their high frequency components. Experimental results show that the proposed feature selection method outperforms uniform sampling, and that random filters are very competitive with the common Gabor wavelet filters for face recognition tasks

Distributed SVM applied to image classification

This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of atoms that spans the space of the observed signal. Redundant dictionaries allow for sparse representation of the observed signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise