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Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple(More)
Dimensionality reduction methods (DRs) have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new unsupervised DR method called sparsity preserving projections (SPP). Unlike many existing techniques such as local preserving projection (LPP) and neighborhood preserving embedding(More)
Spoofing with photograph or video is one of the most common manner to circumvent a face recognition system. In this paper, we present a real-time and non-intrusive method to address this based on individual images from a generic webcamera. The task is formulated as a binary classification problem, in which, however, the distribution of positive and negative(More)
Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be(More)
Most classical template-based frontal face recognition techniques assume that multiple images per person are available for training, while in many real-world applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local(More)
Single training image face recognition is one of main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA have achieved success in face recognition field, but can not be directly used to the single training image scenario. Recent graph-based semi-supervised dimensionality reduction(More)
One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set,(More)
In this paper, we present a novel approach to deal with the problem of detecting whether the eyes in a given still face image are closed, which has wide potential applications in human–computer interface design, facial expression recognition, driver fatigue detection, and so on. The approach combines the strength of multiple feature sets to characterize the(More)