Human gait-based gender classification using neutral and non-neutral gait sequences

Zhyar Q.  Mawlood; Azhin T.  Sabir

doi:10.15649/2346075X.689

Autores/as

Zhyar Q. Mawlood Zhyar Q. Mawlood, Computer department, College of engineering, Al-kitab University College, Altin copre, kirkuk, Iraq
Azhin T. Sabir Azhin T. Sabir, Department of Software Engineering, FENG, Koya University, Koya, Iraq

DOI:

https://doi.org/10.15649/2346075X.689

Palabras clave:

Gait; Gait Entropy Energy Image; k-NN; SVM

Resumen

A biometric system offers automatic identification of an individual based
on characteristic possessed by the individual. Biometric identification systems are often categorized as physiological or behavioural characteristics.
Gait as one of the behavioural biometric recognition aims to recognize
an individual by the way he/she walk. In this paper we propose gender
classification based on human gait features using wavelet transform and
investigates the problem of non-neutral gait sequences; Coat Wearing and
carrying bag condition as addition to the neutral gait sequences. We shall
investigate a new set of feature that generated based on the Gait Energy Image and Gait Entropy Image called Gait Entropy Energy Image
(GEnEI). Three different feature sets constructed from GEnEI based
on wavelet transform called, Approximation coefficient Gait Entropy
Energy Image, Vertical coefficient Gait Entropy Energy Image and Approximation & Vertical coefficients Gait Entropy Energy Image Finally
two different classification methods are used to test the performance of
the proposed method separately, called k-nearest-neighbour and Support
Vector Machine. Our tests are based on a large number of experiments
using a well-known gait database called CASIA B gait database, includes
124 subjects (93 males and 31 females). The experimental result indicates
that the proposed method provides significant results and outperform the
state of the art.

Biografía del autor/a

Zhyar Q. Mawlood, Zhyar Q. Mawlood, Computer department, College of engineering, Al-kitab University College, Altin copre, kirkuk, Iraq

Zhyar Q. Mawlood, Computer department, College of engineering, Al-kitab University College, Altin copre, kirkuk, Iraq

Azhin T. Sabir, Azhin T. Sabir, Department of Software Engineering, FENG, Koya University, Koya, Iraq

Azhin T. Sabir, Department of Software Engineering, FENG, Koya University, Koya, Iraq

Referencias

Choudhary J. Survey of Different Biometrics Techniques. International Journal of Modern Engineering Research (IJMER). 2012: p. 2249--6645.

Liu, Ye , Li , Zhang F, Lin L. Memory-based Gait Recognition. In BMVC.; 2016. https://doi.org/10.5244/C.30.82

Sabir , Al-jawad , Jassim S. Gait recognition using spatio-temporal silhouette-based features. In Mobile Multimedia/Image Processing, Security, and Applications 2013}; 2013; Baltimore, Maryland, United States. https://doi.org/10.1117/12.2017950

Sabir , Ahmed H, Faeq K, Maghdid S. Human gait identification using Kinect sensor. Kurdistan Journal of Applied Research. 2017; 2: p. 142—146. https://doi.org/10.24017/science.2017.3.37

Ahmed H, Sabir T. Human Gender Classification Based on Gait Features Using Kinect Sensor. In 3rd IEEE International Conference on Cybernetics (CYBCONF); 2017; Exeter, UK. https://doi.org/10.1109/CYBConf.2017.7985782

Mather , Murdoch L. Gender discrimination in biological motion displays based on dynamic cues. Proc. R. Soc. Lond. B. 1994; 258: p. 273--279. https://doi.org/10.1098/rspb.1994.0173

Kozlowski LT, Cutting JE. Recognizing the sex of a walker from a dynamic point-light display. Perception & psychophysics. 1977; 21(6): p. 575-580. https://doi.org/10.3758/BF03198740

Kastaniotis D, Theodorakopoulos I, Economou GaFS. Gait-based gender recognition using pose information for real time applications. In 2013 18th International Conference on Digital Signal Processing (DSP).: IEEE; 2013. https://doi.org/10.1109/ICDSP.2013.6622766

Andersson VO, Amaral LS, Tonini AR, Araujo RM. Gender and body mass index classification using a microsoft kinect sensor. In The Twenty-Eighth International Flairs Conference; 2015.

Gianaria E, Grangetto M, Lucenteforte M, Balossino N. Human classification using gait features. In International workshop on biometric authentication, Springer; 2014. p. 16--27. https://doi.org/10.1007/978-3-319-13386-7_2

Lee , Grimson WEL. Gait analysis for recognition and classiﬁcation. In Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002 ; 155--162; Washington, DC, USA: Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on.

Wang L. Behavioral Biometrics For Human identification:Intelligent Application: Intelligent Applications. IGI Global,; Aug 31, 2009. https://doi.org/10.4018/978-1-60566-725-6

Yu , Tan , Huang , Jia , Wu X. A study on gait-based gender classiﬁcation. IEEE Transactions on image processing. 2009; 18(8): p. 1905—1910. https://doi.org/10.1109/TIP.2009.2020535

Jalil SZA, Taib MN, Abdullah H, Yunus MM. Frequency Radiation Characteristic Around The Human Body. International Journal of Simulation: Systems, Science & Technology. 2011; 12(1): p. 34--39.

Hu M, Wang , Zhang Z, Wang Y. Combining spatial and temporal information for gait based gender classification. In 20th International Conference on Pattern Recognition (ICPR); 2010; Istanbul, Turkey. https://doi.org/10.1109/ICPR.2010.897

Sabir , Al-Jawad , Jassim S. Feature selection gait-based gender classification under different circumstances. In Proc. Real-Time Image and Video Processing, SPIE. ; 2014; Brussels, Belgium. https://doi.org/10.1117/12.2052586

Piccardi M. Background subtraction techniques: a review. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583; 2004: IEEE International Conference on Systems. p. 3099--3104.