FACE RECOGNITION WITH WEIGHT VARIATIONS

In human Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition system. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using different deep learning architectures, namely, sparse-stacked denoising autoencoders .We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on theYALE database show that the introduction of weight aware regularization improves the identication accuracy of the architectures both with and without dropout.