3D face registration using multiple average models

Abstract

Three dimensional (3D) face recognition is a frequently used biometric method and its performance is substantially dependent on the accuracy of registration. In this work, we explore registration techniques. Registration aligns two faces and make a comparison possible between the two surfaces. In the literature, best results have been achieved by a one-to-all approach, where a test face is aligned to each gallery face separately. Unfortunately, the computational cost of this approach is high. To overcome the computational bottleneck, we examine registration based on an Average Face Model (AFM). We propose a better method for the construction of an AFM. To improve the registration, we propose to group faces and register with category-specific AFMs. We compare the groups formed by clustering in the face space with the groups based on morphology and gender. We see that gender and morphology classes exist, when faces are categorized with the clustering approach. As a result of registering via an AFM, it is possible to apply regular re-sampling on the depth values. With regular re-sampling, improvements in recognition performance and comparison time were obtained. As another factor causing diversity in the face space, we explore expression variations. To reduce the negative effect of expression in registration and recognition, we propose a region-based registration method. We divide the facial surface into several logical segments, and for each segment we create an Average Region Model (ARM). Registering via each ARM separately, we examine regional recognition performance. We see that even though some regions such as nose or eye area are less affected by expression variations, no single region is sufficient by itself and the use of all regions is beneficial in recognition. We experiment with several fusion techniques to combine results from individual regions and obtain performance increase.