Recognition of Face Biometrics

Abstract

In the proposed chapter, a novel, effective, and efficient approach to face recognition is presented. It is a fusion of both global and local features of images, which significantly achieves higher recognition. Initially, the global features of images are determined using polar cosine transforms (PCTs), which exhibit very less computation complexity as compared to other global feature extractors. For local features, the rotation invariant local ternary patterns are used rather than using the existing ones, which help improving the recognition rate and are in alignment with the rotation invariant property of PCTs. The fusion of both acquired global and local features is performed by mapping their features into a common domain. Finally, the proposed hybrid approach provides a robust feature set for face recognition. The experiments are performed on benchmark face databases, representing various expressions of facial images. The results of extensive set of experiments reveal the supremacy of the proposed method over other approaches in terms of efficiency and recognition results.