Dictionary based framework for face recognition, designed mutually for single training sample (STS) and degraded set (DS)

Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled by the single representative sample per class. Variations between the training samples and a query image are captured by the intra-class variation dictionary. Misalignment of the query image is handled by aligning it with respect to the representative samples. A few moderately aligned and warped face images obtained from the query image are then sparsely represented using the data dictionary with additional constraints on their variance which reduces the obligation of a perfectly aligned query image. The experiments results on AR and LFW datasets validate our claim of superior performance in STS and DS as compared to the other recent methods.