Gender Classification Using a Novel Gait Template: Radon Transform of Mean Gait Energy Image

2011 ◽  
pp. 161-169 ◽  
Author(s):  
Farhad Bagher Oskuie ◽  
Karim Faez
Keyword(s):  
Radon Transform ◽  
Gender Classification ◽  
Gait Energy Image

scholarly journals Comparative Study of Gait Gender Identification using Gait Energy Image (GEI) and Gait Information Image (GII)

2018 ◽  
Vol 197 ◽  
pp. 15006 ◽  
Author(s):  
Rosa Andrie Asmara ◽  
Irtafa Masruri ◽  
Cahya Rahmad ◽  
Indrazno Siradjuddin ◽  
Erfan Rohadi ◽  
...  
Keyword(s):  
Gait Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Gender Classification ◽  
Gender Identification ◽  
Walking Gait ◽  
Gait Energy Image ◽  
Surveillance Technology ◽  
Gender Based ◽  
Information Image

Identifying gender from the pedestrian video is one crucial key to study demographics in such areas. With current video surveillance technology, identifying gender from a distance is possible. This research proposed the utilization of computer vision to identify gender based on their walking gait. The data feature used to determine gender based on their walking gait divided into five parts, namely the head, chest, back, waist & buttocks, and legs. Two different methods are used to perform the real-time gender gait recognition process, i.e., Gait Energy Image (GEI) and Gait Information Image (GII), while the Support Vector Machine (SVM) method used as the data classifier. The experimental results show that the process of identifying gender based on walking with GEI method is 55% accuracy and GII method is 60% accuracy. From these results, it can conclude that the method GII with SVM classifier has the best accuracy in the process of gender classification

Gender Classification Using Neck Features Extracted From Profile Images

2014 ◽  
Vol 3 (2) ◽  
pp. 1-6
Author(s):  
Munehiro Nakamura ◽  
Kengo Iwata ◽  
Masatoshi Kimura ◽  
Haruhiko Kimura
Keyword(s):  
Gender Classification

COMPARISON OF PROGRAMMING LANGUAGES FOR HUMAN GENDER CLASSIFICATION

2017 ◽  
Vol 5 (2) ◽  
pp. 34
Author(s):  
KISHORE G.D.K. ◽  
REDDY M. BABU ◽  
◽  
Keyword(s):  
Programming Languages ◽  
Gender Classification
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