Face recognition under partial occlusion and noise

The human face is one of the most popular characteristic which can be used in the biometric security system to identify or verify a user. Face is an acceptable biometric modality because it can be captured from a distance, even without physical contact of the user being identified. Thus the identification or verification does not require cooperation of the user. Recognition systems based on human face are used for a wide variety of applications, due to these benefits. However, the crucial task is still to provide reliable recognition accuracy, but it is a challenging problem under real-world conditions. There have been proposed many methods, but only a few of them are being used in the real-world applications. Even the most recent face recognition algorithms are still facing problems when there is non-ideal imaging, varying illumination, occlusions in the scene or noise of used cameras. We solve these issues within The Next-Generation Hybrid Broadcast Broadband project (HBB-Next) [1]. In this project, we also deal with development of face recognition application, as part of multimodal interface, which will interact with HBB-TV user. In this paper we provide a comparative study of several conventional face recognition methods (PCA a.k.a. Eigenfaces, RBF) and novel kernel methods (KPCA, GDA and SVM) that are suitable to work properly under these conditions. We evaluate the influence of noise and partial occlusion on face recognition accuracy. We are focused on occlusions of eyes and eyebrows as these are the most significant features of a face. Face recognition rates achieved by machine learning methods with accuracy achieved by human perception only are compared. In addition we explore these methods for cases where only a few (up to 4) training samples is available.