Abstract
Video-based face recognition is a challenging research area. In the unconstrained surveillance videos, recognition of faces with arbitrary poses, illumination, expressions, noise and blurriness is highly difficult task. In this paper, an efficient moment invariant face descriptor (MFD) is proposed. The geometric feature points of eyes, nose and mouth are extracted using face block filtering. These descriptors consist of 33 moment invariant features (11 from each). In these 11 moment invariant features, three of them are geometric moments and the remaining eight are central moments. It provides robustness against variability due to changes in localised regions of the faces. A public unconstrained video data set with three cameras is used. The proposed algorithm is compared with LBP, LPQ, HOG and moment invariant feature descriptors against different classifiers such as J48, random forest and multilayer perceptron. Experimental results show that the proposed algorithm using random forest classifier improves face recognition rate.
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