The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models.
Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone. We also present results of human subjects performance on gender classification task and evaluate how the different dimensionality reduction techniques compare with human subjects performance. The results support the psychological plausibility of the global and feature based representation.
Buchala
Samarasena
S
School of Computer Science, University of Hertfordshire, College Lane Hatfield, Herts. AL10 9AB, UK. S.Buchala@herts.ac.uk
Davey
Neil
N
Frank
Ray J
RJ
Loomes
Martin
M
Gale
Tim M
TM
D000465:Algorithms; D003198:Computer Simulation; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012727:Sex Characteristics