The Effect of Distinctiveness in Recognizing Average Face: Human Recognition and Eigenface Based Machine Recognition

Naiwala P. CHANDRASIRI  Ryuta SUZUKI  Nobuyuki WATANABE  Hiroshi YAMADA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.3   pp.514-522
Publication Date: 2013/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.514
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Face Perception and Recognition)
Category: Face Perception and Recognition
Keyword: 
face recognition,  distinctiveness rating,  average face,  principal component analysis (PCA),  eigenface,  

Full Text: PDF(2.3MB)>>
Buy this Article




Summary: 
Face perception and recognition have attracted more attention recently in multidisciplinary fields such as engineering, psychology, neuroscience, etc. with the advances in physical/physiological measurement and data analysis technologies. In this paper, our main interest is building computational models of human face recognition based on psychological experiments. We specially focus on modeling human face recognition characteristics of average face in the dimension of distinctiveness. Psychological experiments were carried out to measure distinctiveness of face images and their results are explained by computer analysis results of the images. Two psychological experiments, 1) Classical experiment of distinctiveness rating and, 2) Novel experiment of recognition of an average face were performed. In the later experiment, we examined on how the average face of two face images was recognized by a human in a similarity test respect to the original images which were utilized for the calculation of the average face. To explain results of the psychological experiments, eigenface spaces were constructed based on Principal Component Analysis (PCA). Significant correlation was found between human and PCA based computer recognition results. Emulation of human recognition of faces is one of the expected applications of this research.