2D+3D face recognition using Dual-tree Wavelet Transform

A new automatic framework is proposed for face recognition and its superiority performance is justified by the FRGC v2 data. Adaboost face detecting method is used for facial region extraction. Then 2D and 3D facial representations which are extracted by the Dual-tree Complex Wavelet Transform (DT-CWT) are introduced to reflect the facial geometry properties in this paper. The level four high-frequency components of 2D texture image and 3D depth image are obtained respectively, and then Linear Discriminant Analysis (LDA) is used to get the feature vectors. Cosine distance is developed for establishing two similarity matrixes respectively. Finally a fusion result is established by the two similarity matrixes. The verification rate at an FAR of 0.1% is 97.6% on All vs. All experiment.