In most situations, identifying humans using faces is an effortless task for humans. Is this true for computers? This very question defines the field of automatic face recognition [10], [38], [79], one of the most active research areas in computer vision, pattern recognition, and image understanding. Over the past decade, the problem of face recognition has attracted substantial attention from ...

Face recognition system is one of the intensive research areas in computer vision and pattern recognition but many of which are focused on recognition of faces under various expressions involving many training sample per class. A constrained optical flow algorithm discussed in this paper recognizes facial images involving various expressions under the condition that one single training sample p...

A new face recognition system is introduced and analyzed in this paper. The system utilizes a novel statistical pattern recognition method to optimize the feature selection process. The optimized feature set feeds a classification module, which is based on the modified ν-support vector machine approach (ν-SVM). The optimized feature set reduces the burden of the subsequent ν-SVM classifier and ...

A major challenge in pattern recognition is labeling of large numbers of samples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression r...

For a family Ω of sets in R and a finite subset S of R, let pΩ(S) be the number of distinct sets of the form S ∩ ω for all ω ∈ Ω. The maximum pattern complexity pΩ(k) is the maximum of pΩ(S) among S with #S = k. The S attaining the maximum is considered as the most effective sampling to distinguish the sets in Ω. We obtain the exact values or at least the order of pΩ(k) in k for various classes...