Authors:
Wen-Shiung Chen
1
;
Wen-Jui Chang
1
;
Lili Hsieh
2
and
Zong-Yi Lin
1
Affiliations:
1
National Chi Nan University, Taiwan
;
2
Hsiuping University of Science and Technology, Taiwan
Keyword(s):
Biometrics, Face Recognition, Gender Classification, Face Detection, Hair Detection, ASM.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing and Artificial Vision Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper, a component-based gender classification based on hair and facial geometrical features are presented. By way of these preprocessing, hair and facial geometry features can then be extracted automatically from the face images. We compare hair detection methods by examining their color and texture features, and also analyze some geometrical features from references. The best performance of 87.15% in gender classification rate is achieved by combining the most significant hair and geometrical features which is better than some of the literature before.