DocumentCode :
3646485
Title :
Gender recognition from gait using RIT and CIT approaches
Author :
İlke Tunalı;Nurettin Şenyer
Author_Institution :
Bilgisayar Mü
fYear :
2012
fDate :
4/1/2012 12:00:00 AM
Firstpage :
1
Lastpage :
4
Abstract :
Gait analyses have been subject to many researches recent years. Previous studies have shown that, gait is a unique biometric data for each person. Based on this, scientists have realized that it is possible to make gender classification from gait. In this study, the feature vectors were extracted from the RIT´s and CIT´s of the binary silhouette images of human gait scenes. These feature vectors were used in the Support Vector Machine (SVM) and Linear Vector Quantization (LVQ) classifiers for gender recognition. Gait data of 100 persons were divided into k-fold as learning and testing data for cross validation. By using 5 cross folds in trails, in average 95.2% true classification success rate was obtained with LVQ while in average 99.3% true classification success rate was obtained with SVM.
Keywords :
"Humans","Pattern recognition","Feature extraction","Support vector machine classification","Retina","Neural networks"
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2012 20th
Print_ISBN :
978-1-4673-0055-1
Type :
conf
DOI :
10.1109/SIU.2012.6204500
Filename :
6204500
Link To Document :
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