Application of maximized inter-class variance for gender classification using RGB-depth camera

This study presents an approach for gender recognition problem in 3D space. Studies have shown that anatomic cues supply distinctive information for visual gender recognition. Vertical, horizontal and depth coordinates of 20 different joints of a walking pedestrian are extracted. Intra-class based m...

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Bibliographic Details
Published in2017 17th International Conference on Control, Automation and Systems (ICCAS) pp. 1433 - 1435
Main Authors Yildirim, Mustafa Eren, Ince, Omer Faruk, Ince, Ibrahim Furkan, Salman, Yucel Batu, Jang-Sik Park, Byung-Woo Yoon
Format Conference Proceeding
LanguageEnglish
Published Institute of Control, Robotics and Systems - ICROS 01.10.2017
Subjects
body joints
depth information
Feature extraction
feature reduction
Gender recognition
Genetic algorithms
Hidden Markov models
Legged locomotion
Microsoft Kinect
Multilayer perceptrons
Three-dimensional displays
Training
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Summary:This study presents an approach for gender recognition problem in 3D space. Studies have shown that anatomic cues supply distinctive information for visual gender recognition. Vertical, horizontal and depth coordinates of 20 different joints of a walking pedestrian are extracted. Intra-class based means of the whole of 60 features are calculated. In next step, absolute differences between the mean values of male and female class features are found. The ones with differences higher than a predefined percentage are considered as distinctive features and used for class representation. We conducted our experiments and benchmark with Genetic Algorithm on a publicly available dataset UPCV gait captured with Microsoft Kinect, consisting of 5 gait sequences from 30 people. We used Multilayer Perceptron for training and testing. According to the results, our method shows a higher performance in less computation time.
DOI:10.23919/ICCAS.2017.8204215