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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Vydáno v:2014 12th International Conference on Signal Processing (ICSP) s. 1263 - 1267
Hlavní autoři: Lijia Lu, Weiyang Liu, Yandong Wen, Yuexian Zou
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2014
Témata:
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
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Abstract 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.
AbstractList 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.
Author Lijia Lu
Weiyang Liu
Yuexian Zou
Yandong Wen
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  surname: Weiyang Liu
  fullname: Weiyang Liu
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  surname: Yandong Wen
  fullname: Yandong Wen
  email: wen.yandong@mail.scut.edu.cn
  organization: Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
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  surname: Yuexian Zou
  fullname: Yuexian Zou
  email: zouyx@pkusz.edu.cn
  organization: Sch. of Electron. & Comput. Eng., Peking Univ., Beijing, China
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Snippet Many intelligent systems are required to deal with the situation of human-computer interaction. As one of the most important front ends, gender classification...
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SubjectTerms Ada-Boost algorithm
Automatical gender detection
Classification algorithms
Collaborative representation classifier
Detectors
Face
Feature extraction
Real-time system
Real-time systems
Robustness
Title Automatical gender detection for unconstrained video sequences based on collaborative representation
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