We present an approach to recognizing faces with varying appearances which also considers the relative probability of occurrence for each appearance. We propose and demonstrate ex...
Nathan Mekuz, Christian Bauckhage, John K. Tsotsos
In this paper, we propose a novel classification method, called nearest intra-class space (NICS), for face recognition. In our method, the distribution of face patterns of each pe...
Techniques for face recognition generally fall into global and local approaches, with the principal component analysis (PCA) being the most prominent global approach. This paper u...
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA ...
Achieving high accuracy in the presence of expression variation remains one of the most challenging aspects of 3D face recognition. In this paper, we propose a novel recognition a...