Face recognition using Zernike and complex Zernike moment features
Chandan Singh, Neerja Mittal, Ekta Walia · Pattern Recognition and Image Analysis · 2011
Selection of a good feature extraction method is the most important factor in achieving the higher recognition rate in face recognition. This paper presents the analysis of two moment based feature extraction methods namely Zernike moments (ZMs) and Complex Zernike moments (CZMs) in application to face image recognition. We have intensively analyzed these methods in terms of their reconstruction ability and invariance to rotation, scale and size. Almost all existing methods use only magnitude component of the moments as invariant features in recognition task. Recently it is proposed that the phase component of moments also captures useful information for image representation. In this paper, we have analyzed the performance of both magnitude and phase coefficients of ZMs and call it CZMs. These methods are tested separately on suitable databases. The databases used are UMIST pose database for rotation variation, JAFFE expression database for size and scale variations, and popular ORL and FERET databases for comparison of recognition results. It can be concluded from the experimental results that the performance of CZMs is not only better than ZMs but also it is the descriptor that gives best recognition rate amongst the descriptors well known for face recognition.