Facial Template Synthesis based on SIFT Features

This paper proposes a procedure for facial template synthesis based on features extracted from multiple facial instances with varying pose. The proposed system extracts the rotation, scale and translation invariant SIFT features, also having high discrimination ability, from the frontal and half left and right profiles of an individual face images. These affine invariant features obviate the need of ad-hoc algorithms to register features of side profiles against frontal profiles for feature-set augmentation. An augmented feature set is then formed from the fusion of features from frontal and side profiles of an individual, after removing feature redundancy. The augmented feature sets of database and query images are matched using the Euclidean distance and Point pattern matching techniques. The experimental results are compared with the system using only frontal face images for both the matching strategies. The reported results prove the efficacy of the proposed system.