Gender recognition from gait using RIT and CIT approaches RIT ve CIT yaklaşimlariyla yürüyüş üzeri̇nden ci̇nsi̇yet tespi̇ti̇

Tunali I., Şenyer N.

2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Fethiye, Mugla, Turkey, 18 - 20 April 2012 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2012.6204500
  • City: Fethiye, Mugla
  • Country: Turkey
  • Ondokuz Mayıs University Affiliated: Yes


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. © 2012 IEEE.