CHOG Based EFD for Geometric Shape Retrieval of Images for Cloth and Object Invariant Gait Recognition
Tejas K. Rayangoudar1, H. C. Nagraj2
1Tejas K. Rayangoudar , Asst. Professor at K.L.E. Institute of Technology, Hubli, India.
2H. C. Nagraj, Principal of NMIT Bangalore, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11887-11892 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9597118419/2019©BEIESP | DOI: 10.35940/ijrte.D9597.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Gait refers to person identification based on the observation of human walking style. One of the prominent hurdles in gait recognition is, the challenges posed by change in apparels like clothes and object held by the subject. The paper explores the feature extraction techniques like CHOG and Elliptical Fourier Descriptors in spatial and frequency domain respectively to mitigate this negative impact on gait recognition. The CHOG behavioural feature extraction technique is used to capture the effective distribution of local gradient on gait sequence images. Further the Elliptical Fourier Descriptor (EFD) is found in frequency domain to access the geometric characteristics of a spatial domain image. The work is carried out on 36 subjects with 5 different apparels and 3 different objects each with 6 gait cycles from standard dataset CASIA SET – B and CMU – MoBo. SVM classifier is used to effectively discriminate the gait cycle patterns using optimal hyper plane. The results obtained have given an improvement of 7% to 24% increase in gait recognition over earlier techniques like GEI, CDA, LDA, ENTROPY, static and dynamic region splitting.
Keywords: Circular Histogram of Oriented gradient(CHOG), Gait cycle, EFD, Silhouette.
Scope of the Article: Image Processing and Pattern Recognition.