Pose‐invariant face recognition using curvelet neural network

Abstract

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.