Expression-invariant face recognition using depth and intensity dual-tree complex wavelet transform features

Abstract

A new multimodal expression-invariant face recognition method is proposed by extracting features of rigid and semirigid regions of the face which are less affected by facial expressions. Dual-tree complex wavelet transform is applied in one decomposition level to extract the desired feature from range and intensity images by transforming the regions into eight subimages, consisting of six band-pass subimages to represent face details and two low-pass subimages to represent face approximates. The support vector machine has been used to classify both feature fusion and score fusion modes. To test the algorithm, BU-3DFE and FRGC v2.0 datasets have been selected. The BU-3DFE dataset was tested by low intensity versus high intensity and high intensity versus low intensity strategies using all expressions in both training and testing stages in different levels. Findings include the best rank-1 identification rate of 99.8% and verification rate of 100% at a 0.1% false acceptance rate. The FRGC v2.0 was tested by the neutral versus non-neutral strategy, which applies images without expression in training and with expression in the testing stage, thereby achieving the best rank-1 identification rate of 93.5% and verification rate of 97.4% at a 0.1% false acceptance rate.