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Component-Based Recognition of Facesand Facial Expressions

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3 Author(s)
Taheri, S. ; Digital Signal Corp., VA, USA ; Patel, V.M. ; Chellappa, R.

Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face data sets show the effectiveness of our method.

Published in:

Affective Computing, IEEE Transactions on  (Volume:4 ,  Issue: 4 )

Date of Publication:

Oct.-Dec. 2013

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