Extraction and fusion of partial face features for cancelable identity verification

In this paper, we propose to extract localized random features directly from partial face image matrix for cancelable identity verification. Essentially, the extracted random features consist of compressed horizontal and vertical facial information obtained from a structured projection of the raw face images. For template security reason, the face appearance information is concealed via averaging several templates over different transformations. The match score outputs of these cancelable templates are then fused through a total error rate minimization. Extensive experiments were carried out to evaluate and benchmark the performance of the proposed method based on the AR, FERET, ORL, Sheffield and BERC databases. Our empirical results show encouraging performances in terms of verification accuracy as well as satisfying four cancelable biometric properties.

► This work proposes to extract directional image features from partial face images.
► The extracted partial templates are averaged for face appearance concealment.
► The extracted features are fused at score level for performance enhancement.
► The proposed method is empirically shown to satisfy the four cancelable properties.