Abstract
In digital image processing, orthogonal moments play an important role in image reconstruction, comparison and enhancement. Orthogonal Moments are derived from the polynomials which are mutually perpendicular and may be discrete or continuous. Most prominent continuous orthogonal moments include Legendre, Zernike and Pseudo-Zernike Moments and primary discrete orthogonal moments are Krawtchouk and Tchebichef Moments. These moments involve minimum information redundancy and therefore can be applied to various fields like face recognition, edge detection, palm print verification and content based retrieval. This paper presents the comparative analysis of these moments in the field of face recognition. Due to robustness to image noise, invariance to rotation, scaling and transformation, these moments give better results than nonorthogonal moments. The paper summarizes the work done by various authors in face recognition field.
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