Three-dimensional human face recognition

In this communication, we propose a technique for three-dimensional (3D) human face recognition. The 3D shape information of the faces is utilized to synthesize two-dimensional spatial functions, called the signature functions. Face recognition task is completed by carrying out cross-correlation between the signature functions of the faces and analyzing the correlation peaks. High correlation peak signifies true class recognition and low or no peak signifies false class rejection. Preliminary experimental results are presented that demonstrate the feasibility.