Fusion of appearance-based face recognition algorithms

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Abstract:

Although many algorithms have been proposed, face recognition and verification systems can guarantee a good level of performances only for controlled environments. In order to improve performances and robustness of face recognition and verification systems, multi-modal and mono-modal systems based on the fusion of multiple recognisers using different or similar biometrics have been proposed, especially for verification purposes. In this paper, a recognition and verification system based on the combination of two well-known appearance-based representations of the face, namely, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Both PCA and LDA are used as feature extractors from frontal-view images. The benefits of such fusion are shown for different environmental conditions, namely, “ideal” conditions, characterised by a very limited variability of environmental parameters, and “real” conditions with large variability of lighting, scale and face expression.

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