HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION
Sídnei A. Drovetto, Luciano Tavares da Silva, Olga Regina Pereira Bellon · 2008
In this paper we propose to perform 3D face matching based on alignments obtained using Simulated Annealing (SA) algorithm guided by the Mean Squared Error (MSE) with M-estimator Sample Consensus (MSAC) and the Surface Interpenetration Measure (SIM). The matching score is obtained by calculation of the SIM after the registration process. Since the SIM is a sensitive measure, it needs a good alignment to give relevance to its value. Our registration approach tends to reach a near global solution and, therefore, produces the necessary precise alignments. By analyzing the matching score, the system can identify if the input images come from the same subject or not. In a verification scenario, we use a hierarchical evaluation model which maximizes the results and reduces the computing time. Extensive experiments were performed on the wellknown Face Recognition Grand Challenge (FRGC) v2.0 3D face database using five different facial regions: three regions of the nose; the region of the eyes; and the face itself. Compared to state-of-the-art works, our approach has achieved a high rank-one recognition rate and a high verification rate.