Adaptive Appearance Based Face Recognition

In this paper, we present an adaptive appearance based face recognition framework that combines the efficiency of global approaches and the robustness of local approaches together. The framework uses a novel eye locator to select an appropriate scheme for appearance based recognition. The eye locator first locates eye candidates via a new strength assignment, determined by the dissimilarity between the local appearance of an image point and the appearance of its neighboring points. Then the eye locator applies a simple but flexible model (half-circle snake) to the local context of the eye candidates in order to either refine the location of an eye candidate or discard non-eye candidates. We show the performance of our framework by testing on challenging face datasets containing extreme expressions, severe occlusions, and varied lighting conditions

1 Introduction

Global appearance based recognition is a popular scheme in recognizing facial identities [23]. With certain subspace representations, such as Eigenface [19], Fisher-face [2], a global face appearance can be effectively “encoded” into a very low dimensional vector for recognition, and thus brings appealing efficiency in inquiring face images. Extensive previous study has shown that applying subspace methods to global appearances can achieve high recognition efficiency with satisfying recognition accuracy, if faces do not contain significant local outlier appearances. Global approaches require accurate face alignment, which can be obtained by eye localization. Automatic eye localization has received extensive study [22], [14], [20], [1]; however, it still is a challenging problem to locate eyes under varied imaging conditions, such as severe reflection of eye-glasses and sun-glasses occlusions. Furthermore, severe local outlier appearances (such as the two above) can also significantly degrade the effectiveness of subspace methods in encoding the global appearances for face recognition [8].