Measuring the Performance of Face Localization Systems

The purpose of Face localization is to determine the coordinates of a face in a given image. It is a fundamental research area in computer vision because it serves, as a necessary first step, any face processing systems, such as automatic face recognition, face tracking or expression analysis. Most of these techniques assume, in general, that the face region has been perfectly localized. Therefore, their performances depend widely on the accuracy of the face localization process. The purpose of this paper is to mainly show that the error made during the localization process may have different impacts which depend on the final application. We first show the influence of localization errors on the specific task of face verification and then empirically demonstrate the problems of current localization performance measures when applied to this task. In order to properly evaluate the performance of a face localization algorithm, we then propose to {\em embed} the final application (here face verification) into the performance measuring process. Using two benchmark databases, BANCA and XM2VTS, we proceed by showing empirically that our proposed method to evaluate localization algorithms better matches the final verification performance

Eye detection using discriminatory features and an efficient support vector machine

Accurate and efficient eye detection has broad applications in computer vision, machine learning, and pattern recognition. This dissertation presents a number of accurate and efficient eye detection methods using various discriminatory features and a new efficient Support Vector Machine (eSVM). This dissertation first introduces five popular image representation methods - the gray-scale image representation, the color image representation, the 2D Haar wavelet image representation, the Histograms of Oriented Gradients (HOG) image representation, and the Local Binary Patterns (LBP) image representation - and then applies these methods to derive five types of discriminatory features. Comparative assessments are then presented to evaluate the performance of these discriminatory features on the problem of eye detection. This dissertation further proposes two discriminatory feature extraction (DFE) methods for eye detection. The first DFE method, discriminant component analysis (DCA), improves upon the popular principal component analysis (PCA) method. The PCA method can derive the optimal features for data representation but not for classification. In contrast, the DCA method, which applies a new criterion vector that is defined on two novel measure vectors, derives the optimal discriminatory features in the whitened PCA space for two-class classification problems. The second DFE method, clustering-based discriminant analysis (CDA), improves upon the popular Fisher linear discriminant (FLD) method. A major disadvantage of the FLD is that it may not be able to extract adequate features in order to achieve satisfactory performance, especially for two-class problems. To address this problem, three CDA models (CDA-1, -2, and -3) are proposed by taking advantage of the clustering technique. For every CDA model anew between-cluster scatter matrix is defined. The CDA method thus can derive adequate features to achieve satisfactory performance for eye detection. Furthermore, the clustering nature of the three CDA models and the nonparametric nature of the CDA-2 and -3 models can further improve the detection performance upon the conventional FLD method. This dissertation finally presents a new efficient Support Vector Machine (eSVM) for eye detection that improves the computational efficiency of the conventional Support Vector Machine (SVM). The eSVM first defines a Θ set that consists of the training samples on the wrong side of their margin derived from the conventional soft-margin SVM. The Θ set plays an important role in controlling the generalization performance of the eSVM. The eSVM then introduces only a single slack variable for all the training samples in the Θ set, and as a result, only a very small number of those samples in the Θ set become support vectors. The eSVM hence significantly reduces the number of support vectors and improves the computational efficiency without sacrificing the generalization performance. A modified Sequential Minimal Optimization (SMO) algorithm is then presented to solve the large Quadratic Programming (QP) problem defined in the optimization of the eSVM. Three large-scale face databases, the Face Recognition Grand challenge (FRGC) version 2 database, the BioID database, and the FERET database, are applied to evaluate the proposed eye detection methods. Experimental results show the effectiveness of the proposed methods that improve upon some state-of-the-art eye detection methods

Face Detection and Verification using Local Binary Patterns

This thesis proposes a robust Automatic Face Verification (AFV) system using Local Binary Patterns (LBP). AFV is mainly composed of two modules: Face Detection (FD) and Face Verification (FV). The purpose of FD is to determine whether there are any face in an image, while FV involves confirming or denying the identity claimed by a person. The contributions of this thesis are the following: 1) a real-time multiview FD system which is robust to illumination and partial occlusion, 2) a FV system based on the adaptation of LBP features, 3) an extensive study of the performance evaluation of FD algorithms and in particular the effect of FD errors on FV performance. The first part of the thesis addresses the problem of frontal FD. We introduce the system of Viola and Jones which is the first real-time frontal face detector. One of its limitations is the sensitivity to local lighting variations and partial occlusion of the face. In order to cope with these limitations, we propose to use LBP features. Special emphasis is given to the scanning process and to the merging of overlapped detections, because both have a significant impact on the performance. We then extend our frontal FD module to multiview FD. In the second part, we present a novel generative approach for FV, based on an LBP description of the face. The main advantages compared to previous approaches are a very fast and simple training procedure and robustness to bad lighting conditions. In the third part, we address the problem of estimating the quality of FD. We first show the influence of FD errors on the FV task and then empirically demonstrate the limitations of current detection measures when applied to this task. In order to properly evaluate the performance of a face detection module, we propose to embed the FV into the performance measuring process. We show empirically that the proposed methodology better matches the final FV performance