On Sparse Point Representation for Face Localisation and Recognition

Abstract

Automatic face recognition has been an active research field during the last few decades. Existing face recognition systems have demonstrated acceptable recognition performance under controlled conditions. However, practical and robust face recognition which is tolerant to various interferential variations remains a difficult and unsolved problem in the research community. In the first part of this thesis, we propose to use the concept of sparse point representation to address four important challenges in face recognition: wider-range tolerance to pose variation, face misalignment, facial landmark localisation and head pose estimation. The sparse point representation can be classified into two different categories. In the first category, equal numbers of feature points are predefined on different individuals. Each feature point refers to a specific physical location on a face while all the feature points have explicit correspondence across different individuals. In the second category, a set of feature points are detected at different locations with discriminative information content on a face image. Both the number and the positions of the feature points are varied from person to person such that diverse facial characteristics of different individuals can be represented. Based on the first category of sparse point representation, we propose a new Constrained Profile Model (CPM) to form an efficient facial landmark localisation framework. We also propose a novel Elastic Energy Model (EEM) to automatically conduct head pose estimation. Based on the second category of sparse point representation, we propose a new Textural Hausdorff Distance (THD), which has demonstrated a considerably wider range of tolerance against both in-depth head rotation and face misalignment. In the second part of this thesis, we focus on recently proposed micropattern based approaches which have proven to outperform classical face recognition methods and provided a new way of investigation into face analysis. We first apply a new Multidirectional Binary Pattern (MBP) representation upon sparse points to establish point correspondences for face recognition. We further propose an enhanced Sobel-LBP operator for face representation, which has demonstrated better performance than the original Local Binary Pattern (LBP). We finally present a novel high-order Local Derivative Pattern (LDP) for face recognition, which can capture more detailed and discriminative information than the first-order local pattern used in LBP. It should be noted that the concept of LDP for face recognition was pioneered by Dr. Baochang Zhang, but we have significantly extended and elaborated this concept. We have extended the concept of LDP from its original usage on Gabor phase features only to much more generalised definition on gray-level images. We have rewritten and enlarged the original draft of his manuscript. Some of the experiments were also implemented and reported by us. In the third part of this thesis, we pay attention to the representation of 'Average Face', which was newly published on Science and claimed to be capable of dramatically improving performance of face recognition systems. To reveal its working mechanism, we conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Our experimental results reveal that the process of face averaging does not necessarily improve all the face recognition systems. Its usefulness is dependent on the specific methods employed in practice.