access icon free Pose-invariant face recognition using curvelet neural network

© The Institution of Engineering and Technology
Received 01/02/2013, Accepted 20/06/2013, Revised 16/05/2013, Published 15/08/2013

A novel pose-invariant face recognition method is proposed by combining curvelet-invariant moments with curvelet neural network. First a special set of statistical coefficients using higher-order moments of curvelet are extracted as the feature vector and then the invariant features are fed into curvelet neural networks. Finally, supervised invariant face recognition is achieved by converging the neural network using curvelet as the activation function of the hidden layer neurons. The experimental results demonstrate that curvelet higher-order moments and curvelet neural networks achieve higher accuracy for face recognition across pose and converge rapidly than standard back propagation neural networks.

Inspec keywords: statistical analysis; neural nets; face recognition; feature extraction; curvelet transforms

Other keywords: feature vector extraction; curvelet neural network; curvelet higher-order moments; novel pose-invariant face recognition method; statistical coefficients; hidden layer neurons; curvelet-invariant moments

Subjects: Other topics in statistics; Other topics in statistics; Integral transforms; Neural computing techniques; Computer vision and image processing techniques; Integral transforms; Image recognition

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