Automatical gender detection for unconstrained video sequences based on collaborative representation

Many intelligent systems are required to deal with the situation of human-computer interaction. As one of the most important front ends, gender classification plays an irreplaceable role. For practical use, a real-time robust gender classification system is presented in this paper. The system consis...

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Bibliographic Details
Published in:2014 12th International Conference on Signal Processing (ICSP) pp. 1263 - 1267
Main Authors: Lijia Lu, Weiyang Liu, Yandong Wen, Yuexian Zou
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2014
Subjects:
Ada-Boost algorithm
Automatical gender detection
Classification algorithms
Collaborative representation classifier
Detectors
Face
Feature extraction
Real-time system
Real-time systems
Robustness
ISBN:9781479921881, 1479921882
ISSN:2164-5221
Online Access:Get full text
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Summary:Many intelligent systems are required to deal with the situation of human-computer interaction. As one of the most important front ends, gender classification plays an irreplaceable role. For practical use, a real-time robust gender classification system is presented in this paper. The system consists of three principal modules: image preprocessing, face detector and gender classifier. To enhance the classification accuracy with affordable complexity, Haar-like features and Ada-Boost-trained classifier are applied to the face detector while Eigenface features and collaborative representation classifier are embedded to the gender classifier. Experimental results verify the real-time ability and gender classification accuracy of the proposed system. It is worth mentioning that the system performs well when handling faces with occlusion and complex background.
ISBN:9781479921881
1479921882
ISSN:2164-5221
DOI:10.1109/ICOSP.2014.7015202