Face detection by neural network trained with Zernike moments

Authors:
Mohammed Saaidia;Sylvie Lelandais;Vincent Vigneron;El-Mouldi Bedda
Affiliations:
IBISC FRE CNRS, Université d'Evry Val d'Essonne, Evry Courcouronnes Cedex, France;IBISC FRE CNRS, Université d'Evry Val d'Essonne, Evry Courcouronnes Cedex, France;IBISC FRE CNRS, Université d'Evry Val d'Essonne, Evry Courcouronnes Cedex, France;LAS, Université de Annaba, Annaba, Algerie
Venue:
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Year:
2007

Citing 3
Cited 0

On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient feature extraction method with pseudo-Zernike moment in RBF neural network-based human face recognition system

EURASIP Journal on Applied Signal Processing
Accurate and efficient computation of high order zernike moments

ISVC'05 Proceedings of the First international conference on Advances in Visual Computing

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Abstract

We present in this communication a new method to localize a face in an image. The originality of work presented consists on the use of vectors of geometrical moments like entries to a Forward Back-Propagation neural network which provide at its output layer a vector of co-ordinates in (R,θ) representing pixels surrounding the face contained in the treated image. Very known for their orthogonality and their rotational invariability, the geometrical moments of Zernike are calculated here to form the feature vectors supplied to the input layer of the network. The experimental results of the application of our method on images of the XM2VTS database are presented.