Using Classical and Resampling Methods for Face Recognition based on Quantified Asymmetry Measures

Face recognition has important applications in psychology and biometric-based authentication, which increases the need for developing automatic face identification systems. Psychologists have long been studying the link between symmetry and attractiveness of the human face, but based on qualitative human judgment alone. The use of objective facial asymmetry information in automatic face recognition tasks is relatively new. The current paper presents a statistical analysis of the role of facial asymmetry measures in face recognition, under expression variation. We first describe a baseline classification method and show that the results are comparable with those based on certain popular (non-asymmetry based) classes of features used in computer vision. We find that facial asymmetry further improves upon the classification performance of these popular features by providing complementary information. Next, we consider two resampling methods to improve upon the baseline method used in previous work, and present a detailed comparison study. We demonstrate that resampling methods succeed in obtaining near perfect classification results on a database of 55 individuals, a statistically significant improvement over the baseline method. Results regarding the role of asymmetry of different parts of the face in distinguishing between individuals, expressions and between males and females are also reported as additional aspects of the study.