On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
A novel approach to the fast computation of Zernike moments
Pattern Recognition
Complete invariants for robust face recognition
Pattern Recognition
On the computational aspects of Zernike moments
Image and Vision Computing
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Pattern Recognition Letters
EURASIP Journal on Applied Signal Processing
Face Recognition Using Improved Fast PCA Algorithm
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
Human Face Recognition Using Different Moment Invariants: A Comparative Study
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
Face recognition across pose: A review
Pattern Recognition
Complex Zernike moments features for shape-based image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Constructing PCA Baseline Algorithms to Reevaluate ICA-Based Face-Recognition Performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition
International Journal of Computer Vision and Image Processing
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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.