Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study on gait-based gender classification
IEEE Transactions on Image Processing
A New Approach for Gender Classification Based on Gait Analysis
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
Gender classification based on fusion of multi-view gait sequences
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Combining Spatial and Temporal Information for Gait Based Gender Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Gender classification in human gait using support vector machine
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Any information about people such as their gender may be useful in some secure places; however, in some occasions, it is more appropriate to obtain such information in an unobtrusive manner such as using gait. In this study, we propose a novel method for gender classification using gait template, which is based on Radon Transform of Mean Gait Energy Image (RTMGEI). Robustness against image noises and reducing data dimensionality can be achieved by using Radon Transformation, as well as capturing variations of Mean Gait Energy Images (MGEIs) over their centers. Feature extraction is done by applying Zernike moments to RTMGEIs. Orthogonal property of Zernike moment basis functions guarantee the statistically independence of coefficients in extracted feature vectors. The obtained feature vectors are used to train a Support Vector Machine (SVM). Our method is evaluated on the CASIA database. The maximum Correct Classification Rate (CCR) of 98.94% was achieved for gender classification. Results show that our method outperforms the recently presented works due to its high performance.