Fusion of likelihood ratio classifier with ICP-based matcher for 3D face recognition
Berk Gökberk, Luuk Spreeuwers, Raymond N. J. Veldhuis · University of Twente Research Information · 2009
Three-dimensional (3D) face recognition systems have started to become popu-lar in biometric systems recently. This is due to several factors: i) facial shape characteristics contain discriminative information, ii) availability of practical 3D acquisition devices, and iii) invariance of 3D facial information to several fac-tors such as illumination and pose changes. It has been shown that classical texture-based 2D face classifiers have difficulties in identifying faces under such conditions. Therefore, taking advantage of 3D facial shape information either alone or together with 2D modality is considered to be a viable solution under such circumstances. In this work, we propose a novel 3D face recognition system using the combination of different individual 3D face classifiers; namely Linear Discriminant-based (LDA) Likelihood Ratio classifier with the Iterative Closest Point-based (ICP) matching algorithm. Both systems operate on aligned and normalized 3D facial surfaces. Alignment phase of the proposed system carries out absolute alignment such that all faces are in a specific position and direction, with non-facial parts removed. LDA-based system uses absolutely aligned faces and produce similarity scores using Likelihood ratio-based classifier. However, the ICP-based classifier performs additional surface matching between absolutely aligned faces, which can be considered as relative alignment. After pair-wise alignment of gallery and probe faces, the ICP algorithm produces dissimilarity scores, by measuring the quality of surface registration. With the use of dif-ferent matching algorithms and different alignment methods, our approach tries to minimize the shortcomings of each individual method. Finally, the scores ob-tained by 3D face recognizers are fused to improve the verification accuracy. Our preliminary experiments conducted on the subset of FRGC v2 3D face database show promising performance improvement in verification simulations. 1