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We propose a face verification framework using sparse representations that integrates two ways of employing sparsity. Given an image pair (A, B) and a dictionary D, for image A(B), we generate two sparse codes, one by using the original dictionary and the other by adding B(A) into D as an augmented dictionary. Then the correlation of the sparse codes of A and B, both under the original dictionary...
In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric-Pairs of Pixels (CCS-POP). It concatenates...
With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data...
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