Abstract
This paper proposes a novel method for feature extraction from the curvelet subbands using statistical methods. The face images is decomposed and then in curvelet basis a special set of statistical coefficients using higher order moments is extracted as the feature vector. Curvelet represent the image edges and the curved singularities in images more efficiently than wavelet. In addition, generalized neural network is used for verification to enhance the recognition accuracy which is trained by feature vector. The performance of proposed method is evaluated on two face databases: FERET and ORL. Extensive experimental results and comparison with the existing methods show the effectiveness of the proposed recognition method.
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Sharma, P., Arya, K.V., Yadav, R.N. (2012). Extraction of Facial Features Using Higher Order Moments in Curvelet Transform and Recognition Using Generalized Mean Neural Networks. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_70
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DOI: https://doi.org/10.1007/978-81-322-0491-6_70
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
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