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Gender recognition from unconstrained and articulated human body
1Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China ; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
Thescientificworldjournal
|July 1, 2014
Summary
This study introduces a novel method for gender recognition using articulated human body images in real-world, unconstrained settings. It investigates optimal body parts and representations for accurate, robust gender identification.
Area of Science:
- Computer Vision
- Biometrics
- Machine Learning
Background:
- Traditional gender recognition primarily uses face images in controlled environments.
- Real-world applications require robust gender recognition from unconstrained human body images.
Purpose of the Study:
- To propose and evaluate a novel method for gender recognition from articulated human body images in unconstrained environments.
- To systematically investigate informative body parts, optimal combinations, and effective representations for body-based gender recognition.
- To explore data fusion schemes and feature dimensionality reduction techniques.
Main Methods:
- Utilizing articulated human body images from unconstrained, real-world environments.
- Conducting a systematic study on body part informativeness and feature representation.
- Implementing data fusion strategies and partial least squares for feature dimensionality reduction.
Main Results:
- Demonstrated the feasibility of gender recognition using articulated body images in unconstrained settings.
- Identified key body parts and representations that enhance recognition accuracy.
- Validated the proposed method on two novel, unconstrained datasets.
Conclusions:
- Body-based gender recognition in unconstrained environments is achievable and practical.
- The proposed method offers a robust approach for gender identification beyond facial analysis.
- This research opens new avenues for gender recognition in diverse real-world applications.
