Objective 3D face recognition: Evolution, approaches and challenges.
Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without going into technical details. The discussion clearly articulates the justification to establish archival, reference databases to compare and evaluate different strategies.
Smeets
Dirk
D
K.U. Leuven, Medical Imaging Research Center, Faculty of Engineering, Department of Electrical Engineering - ESAT, Center for Processing Speech and Images - PSI, Leuven, Belgium.
Claes
Peter
P
Vandermeulen
Dirk
D
Clement
John Gerald
JG
D056667:Biometric Identification / Q000379:methods*; D016002:Discriminant Analysis; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D006801:Humans; D021621:Imaging, Three-Dimensional; D025341:Principal Component Analysis