Face Recognition Based on Efficient Facial Scale Estimation

Facial recognition technology needs to be robust for arbitrary facial appearances because a face changes according to facial expressions and facial poses. In this paper, we propose a method which automatically performs face recognition for variously scaled facial images. The method performs flexible feature matching using features normalized for facial scale. For normalization, the facial scale is probabilistically estimated and is used as a scale factor of an improved Gabor wavelet transformation. We implement a face recognition system based on the proposed method and demonstrate the advantages of the system through facial recognition experiments. Our method is more efficient than any other and can maintain a high accuracy of face recognition for facial scale variations.

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