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Feature description is one of the most frequently studied areas in the expertsystems and machine learning. Effective encoding of the images is an essentialrequirement for accurate matching. These encoding schemes play a significantrole in recognition and retrieval systems. Facial recognition systems should beeffective enough to accurately recognize individuals under intrinsic andextrinsic variations of the system. The templates or descriptors used in thesesystems encode spatial relationships of the pixels in the local neighbourhoodof an image. Features encoded using these hand crafted descriptors should berobust against variations such as; illumination, background, poses, andexpressions. In this paper a novel hand crafted cascaded asymmetric localpattern (CALP) is proposed for retrieval and recognition facial image. Theproposed descriptor uniquely encodes relationship amongst the neighbouringpixels in horizontal and vertical directions. The proposed encoding scheme hasoptimum feature length and shows significant improvement in accuracy underenvironmental and physiological changes in a facial image. State of the arthand crafted descriptors namely; LBP, LDGP, CSLBP, SLBP and CSLTP are comparedwith the proposed descriptor on most challenging datasets namely; Caltech-face,LFW, and CASIA-face-v5. Result analysis shows that, the proposed descriptoroutperforms state of the art under uncontrolled variations in expressions,background, pose and illumination.

Cascaded Asymmetric Local Pattern: A Novel Descriptor for Unconstrained Facial Image Recognition and Retrieval