DOI : https://doi.org/10.18517/ijaseit.8.4-2.6795

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Siti Zaharah A. Rahman (1), Siti Norul Huda Sheikh Abdullah (2), Khairul Akram Zainol Ariffin (3)
(1) Digital Forensic Research Group, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor Darul Ehsan, Malaysia.
(2) Digital Forensic Research Group, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor Darul Ehsan, Malaysia.
(3) Digital Forensic Research Group, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia 43600 Bangi, Selangor Darul Ehsan, Malaysia.
Fulltext View | Download
How to cite (IJASEIT) :
A. Rahman, Siti Zaharah, et al. “Gait Recognition Based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, Sept. 2018, pp. 1402-10, doi:10.18517/ijaseit.8.4-2.6795.
Citation Format :
Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of a new image representation and the classification. The first phase comprises image binarization process and energy image generation using gait average image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event and our proposed method also outperforms Dalal Method.

Afsar, P., Cortez, P. & Santos, H., “Automatic visual detection of human behavior: A review from 2000 to 2014”, Expert Systems with Applications 42(20): 6935-6956, 2015.

Lv, Z., Xing, X., Wang, K. & Guan, D., “Class energy image analysis for video sensor-based gait recognition: A review”, Sensors (Switzerland) 15(1): 932-964, 2015.

Lee, C. P., Wee, A., Tan, C. & Lim, K. M., “Review on Vision-Based Gait Recognition : Representations, Classification Schemes and Datasets”, American Journal of Applied Science 14(2): 252-266. doi:10.3844/ajassp.2017.252.266, 2017.

A. Saadoon and M. J. Nordin, "An automatic human gait recognition system based on joint angle estimation on silhouette images," Journal of Theoretical and Applied Information Technology, vol. 81, p. 277, 2015

R.A.K. Noaman, M. A. M. Ali, N. Zainal, F. Saeed, “Human Detection Framework for Automated Surveillance Systems”. International Journal of Electrical and Computer Engineering (IJECE), 2016; 6(2): 877~886.

Rabiah Adawiyah Shahad,Mohamad Hanif Md Saad and Aini Hussain,"Activity Recognition for Smart Building Application Using Complex Event Processing Approach," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 2, pp. 315-322, 2018

Basel Alshaikhdeeb and Kamsuriah Ahmad,"Biomedical Named Entity Recognition: A Review," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 889-895, 2016.

S. N. H. S. Abdullah, M. Khalid, and K. Omar, “Performance Comparison of License Plate Recognition System Using Multi-Features and SVM”, Asia-Pacific Journal of Information Technology and Multimedia, pp. 53-62, 2011.

Arora, P., Srivastava, S., Arora, K. and Bareja, S., “Improved Gait Recognition Using Gradient Histogram Gaussian Image”, Procedia Computer Science 58(408-413, 2015.

Dalal, N. & Triggs, B., “Histograms of oriented gradients for human detection”, Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 I: 886-893, 2005.

Han, J., Bhanu, B., “Individual recognition using gait energy energy image”, IEEE Transition Pattern Analysis Machine Intelligent, 28, 316-322), 2006.

Chen, J. & Liu, J., “Average gait differential image based human recognition”, Scientific World Journal, pp. 1-8,2014.

Wang, C., Zhang, J., Pu, J., Yuan, X. and Wang, L., “Chrono-gait image: a novel temporal template for gait recognition”, Computer Vision-ECCV , page: 257-270. Springer, 2010.

Hofmann, M. & Rigoll, G., “Improved Gait Recognition using Gradient Histogram Energy Image”, Proceedings - International Conference on Image Processing, ICIP 1389-1392, 2012.

D. K. Sahu and M. P. Parsai, "Different Image Fusion Technique- A Critical Review," International Journal of Modern Engineering Research (IJMER), Vol. 2, No.5, pp. 4298-4301, 2012.

Luo, J., Zhang, J., Zi, C., Niu, Y., Tian, H. & Xiu, C., “Gait Recognition Using GEI and AFDEI”, International Journal of Optics,2015.

Eleyan, A. & Demirel, H., “Co-occurrence matrix and its statistical features as a new approach for face recognition”, Turk J Elec Eng & Comp Sci 19(1): 97-107, 2011.

A. F. Bobick J. W. Davis, “The recognition of human movement using temporal templates”, Pattern Analysis and Machine Intelligence IEEE Transactions, vol. 23 no. 3 pp. 257-267, 2001.

Chen C., Liang J., Zhao H., Hu H., Tian J., “Frame difference energy image for gait recognition with incomplete silhouettes”, Pattern Recognition Letter,30:977-984, 2009.

I. M. Burhan M. J. Nordin, “Multi-View Gait Recognition Using Enhanced Gait Energy Image and Radon Transform Techniques”, Asian Journal of Applied Sciences,8 no. 2 pp. 138-148, 2015.

Zhang, E., Zhao, Y., & Xiong, W., “Active energy image plus 2DLPP for gait recognition”, Signal Processing, 90, 2295-2302., 2010.

Chen S., Ma T., Huang W., Gao Y., “A multi-layer windows method of moments for gait recognition”,J. Electron. Inf. Technol., 31:116-119, 2009.

Antony, J. J. & Suchetha, M., “Vision Based Vehicle Detection: A Literature Review”, International Journal of Applied Engineering Research ISSN 11(5): 973-4562, Retrieved from http://www.ripublication.com, 2016.

Cai, Z., Yu, P., Liang, Y., Lin, B. & Huang, H., “SVM-KNN Algorithm for Image Classification Based on Enhanced HOG Feature”, Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016 (3): 111-117. doi:10.12792/icisip2016.023, 2016.

Rahman, S. Z. A., Abdullah, S. N. H. S. and Nazri, M. Z. B. A. 2016, “The analysis for Gait Energy Image based on statistical methods”, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), pp. 125-128, 2016.

Shuai Zheng, Junge Zhang, Kaiqi Huang, Ran He and Tieniu Tan., “Robust View Transformation Model for Gait Recognition”, Proceedings of the IEEE International Conference on Image Processing, pp. 2073-2076,2011.

M. Naeimizaghiani, S. N. H. S. Abdullah, F. Pirahansiah, and B. Bataineh, "Character and object recognition based on global feature extraction," Journal of Theoretical and Applied Information Technology, vol. 54, pp. 109-120, 2013.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).