Compact Signatures for 3D Face Recognition under Varying Expressions

We present a novel approach to 3D face recognition using compact face signatures based on automatically detected 3D landmarks. We represent the face geometry with inter-landmark distances within selected regions of interest to achieve robustness to expression variations. The inter-landmark distances are compressed through principal component analysis and linear discriminant analysis is then applied on the reduced features to maximize the separation between face classes. The classification of a probe face is based on a nearest mean classifier after transforming the probe onto the subspace. We analyze the performance of different landmark combinations (signatures) to determine a signature that is robust to expressions. The selected signature is then used to train a point distribution model for the automatic localization of the landmarks, without any prior knowledge of scale, pose, orientation or texture. We evaluate the proposed approach on a challenging publicly available facial expression database (BU-3DFE) and achieve 96.5% recognition rate using the automatically localized signature. Moreover, because of its compactness the face signature can be stored on 2D barcodes and used for radio-frequency identification.