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Statistical features from frame aggregation and differences for human gait recognition

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Abstract

Human gait recognition, an alternate biometric technique, received significant attention in the last decade. As many gait recognition applications require real-time response, the primary concern is to design efficient and straightforward gait features for human recognition. In this work, two novel gait features are proposed. Both features are designed by exploring the dynamic variations of different body parts during a gait cycle. The first feature set is based on one-against-all gait frame differences for person identification. This novel approach divides each frame in a gait cycle to blocks, compute the block sum, and then find the difference of respective block sum between the first frame and the rest. The second feature set is defined on the first-order statistics of the normalized sum of the frames in a cycle. Two other existing features- Centroid of Silhouette frames and feature values defined on Change Energy Images are also considered. Feature level fusion is realized by considering the different combinations of the four types of features. Experiments carried out with the CASIA Gait Dataset B demonstrated the proposal’s merit with high recognition accuracy. The outcome of the investigations is promising when compared to recent contributions.

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References

  1. Andrie BR, Arai A, Kohei (2011) A review of chinese academy of sciences ( CASIA) Gait Database As a Human Gait Recognition Dataset

  2. Anusha R, Jaidhar C (2019) Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor. Multimed Tools Appl 79(3–4):2873–2896. https://doi.org/10.1007/s11042-019-08400-8

    Article  Google Scholar 

  3. Arora, P., Srivastava, S., Arora, K., Bareja, S. (2015) Improved gait recognition using gradient histogram gaussian image. In: Procedia Computer Science,Volume 58. pp. 408–413

  4. Arshad H, Khan MA, Sharif M, Yasmin M, Javed MY (2019) Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int J Mach Learn Cybern 10:3601–3618. https://doi.org/10.1007/s13042-019-00947-0

    Article  Google Scholar 

  5. Bashir, K., Xiang, T., Gong, S. (2009) Gait recognition using gait entropy image. In: 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), London, 2009. pp. 1–6

  6. Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31:2052–2060. https://doi.org/10.1016/j.patrec.2010.05.027

    Article  Google Scholar 

  7. Bouchrika I, Nixon MS (2007) Model-based feature extraction for gait analysis and recognition. In: Gagalowicz A, Philips W (eds) Computer vision/computer graphics collaboration techniques. MIRAGE 2007. Lecture notes in computer science vol 4418. Springer, Berlin, Heidelberg

    Google Scholar 

  8. Chen X, Weng J, Lu W, Xu J (2018) Multi-gait recognition based on attribute discovery. IEEE Trans Pattern Anal Mach Intell 40:1697–1710. https://doi.org/10.1109/TPAMI.2017.2726061

    Article  Google Scholar 

  9. Das, S., Lazarewicz, M., Finkel, L.H (2004) Principal component analysis of temporal and spatial information for human gait recognition. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, 2004. pp. 4568–4571

  10. El-Alfy H, Mitsugami I, Yagi Y (2018) Gait recognition based on Normal distance maps. IEEE Trans Cybern 48:1526–1539. https://doi.org/10.1109/TCYB.2017.2705799

    Article  Google Scholar 

  11. Guru VGM, Kamalesh VN (2018) A robust human gait recognition approach using multi-interval features. Data Anal Learn 43:51–63. https://doi.org/10.1007/978-981-13-2514-4_5

    Article  Google Scholar 

  12. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322. https://doi.org/10.1109/TPAMI.2006.38

    Article  Google Scholar 

  13. He Y, Zhang J, Shan H, Wang L (2019) Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans Inf Forensics Secur 14:102–113. https://doi.org/10.1109/TIFS.2018.2844819

    Article  Google Scholar 

  14. Huang G, Lu Z, Pun C, Cheng L (2020) Flexible gait recognition based on flow regulation of local features between key frames. IEEE Access 8:75381–75392. https://doi.org/10.1109/access.2020.2986554

    Article  Google Scholar 

  15. Janssen D, Schöllhorn WI, Lubienetzki J, Fölling K, Kokenge H, Davids K (2008) Recognition of emotions in gait patterns by means of artificial neural nets. J Nonverbal Behav 32:79–92. https://doi.org/10.1007/s10919-007-0045-3

    Article  Google Scholar 

  16. Johansson G (1975) Visual motion perception. Sci Am 232:76–88. https://doi.org/10.1038/scientificamerican0675-76

    Article  Google Scholar 

  17. Lam THW, Lee RST (2005) A new representation for human gait recognition: motion silhouettes image (MSI). In: Zhang D, Jain AK (eds) Advances in biometrics. ICB 2006. Lecture notes in computer science, vol 3832. Springer, Berlin, Heidelberg

    Google Scholar 

  18. Lee TKM, Belkhatir M, Sanei S (2014) A comprehensive review of past and present vision-based techniques for gait recognition. Multimed Tools Appl 72:2833–2869. https://doi.org/10.1007/s11042-013-1574-x

    Article  Google Scholar 

  19. Lishani AO, Boubchir L, Khalifa E, Bouridane A (2019) Human gait recognition using GEI-based local multi-scale feature descriptors. Multimed Tools Appl 78:5715–5730. https://doi.org/10.1007/s11042-018-5752-8

    Article  Google Scholar 

  20. Liu, Y.Q., Wang, X. (2011) Human gait recognition for multiple views. In: Procedia Engineering. pp. 1832–1836

  21. Mahfouf Z, Merouani HF, Bouchrika I, Harrati N (2018) Investigating the use of motion-based features from optical flow for gait recognition. Neurocomputing. 283:140–149. https://doi.org/10.1016/j.neucom.2017.12.040

    Article  Google Scholar 

  22. Mehmood A, Khan M, Sharif M et al (2020) Prosperous human gait recognition: an end-to-end system based on pre-trained CNN features selection. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08928-0

  23. Mohan Kumar HP, Nagendraswamy HS (2014) Change energy image for gait recognition: an approach based on symbolic representation. Int J Image, Graph Signal Process 6:1–8. https://doi.org/10.5815/ijigsp.2014.04.01

  24. More SA, Deore PJ (2018) Gait recognition by cross wavelet transform and graph model. IEEE/CAA J Autom Sin 5:718–726. https://doi.org/10.1109/JAS.2018.7511081

    Article  Google Scholar 

  25. Nabila M, Mohammed AI, Yousra BJ (2017) Gait-based human age classification using a silhouette model. IET Biometrics 7:116–124. https://doi.org/10.1049/iet-bmt.2016.0176

    Article  Google Scholar 

  26. Protas EJ, Mitchell K, Williams A, Qureshy H, Caroline K, Lai EC (2005) Gait and step training to reduce falls in Parkinson's disease. NeuroRehabilitation. 20:183–190

    Article  Google Scholar 

  27. Shirke, S., S.S.Pawar, Shah K (2014) Literature review: model free human gait recognition. In: 2014 Fourth International Conference on Communication Systems and Network Technologies. pp. 891–895

  28. Sudha LR, Bhavani R (2014) A combined classifier kNN-SVM in gait-based biometric authentication system. Int J Comput Appl Technol 49:113–121. https://doi.org/10.1504/ijcat.2014.060522

    Article  Google Scholar 

  29. Sugandhi, K., Raju, G. (2019) An efficient HOG-centroid descriptor for human gait recognition. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates. pp. 355–360

  30. Sugandhi K, Raju G (2019) Discriminative gait features based on signal properties of Silhouette centroids. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T, Kashyap R (eds) Advances in computing and data sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore

    Google Scholar 

  31. Sugandhi, K., Wahid, F.F., Raju, G. (2017) Feature extraction methods for human gait recognition – A survey. In: Singh M., Gupta P., Tyagi V., Sharma A., Ören T., Grosky W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. pp. 377–385

  32. Sugandhi, K., Fatina Farha, W., Raju, G. (2019) Inter frame statistical feature fusion for human gait recognition. In: 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 2019. pp. 1–5

  33. Sugandhi K, Wahid FF, Nikesh P, Raju G (2019) An overlap-based human gait cycle detection. Int J Biom 11:148–159. https://doi.org/10.1504/IJBM.2019.099033

    Article  Google Scholar 

  34. Wang X, Wang J, Yan K (2018) Gait recognition based on Gabor wavelets and (2D)2PCA. Multimed Tools Appl 77:12545–12561. https://doi.org/10.1007/s11042-017-4903-7

    Article  Google Scholar 

  35. Wang H, Fan Y, Fang B, Dai S (2018) Generalized linear discriminant analysis based on euclidean norm for gait recognition. Int J Mach Learn Cybern 9:569–576. https://doi.org/10.1007/s13042-016-0540-0

    Article  Google Scholar 

  36. Webster, J.G., Makihara, Y., Matovski, D.S., Nixon, M.S., Carter, J.N., Yagi, Y. (2015) Gait recognition: databases, representations, and applications. In: Wiley Encyclopedia of Electrical and Electronics Engineering. pp. 1–15

  37. Xu W, Zhu C, Wang Z (2018) Multiview max-margin subspace learning for cross-view gait recognition. Pattern Recogn Lett 107:75–82. https://doi.org/10.1016/j.patrec.2017.10.033

    Article  Google Scholar 

  38. Yoo JH, Hwang D, Nixon MS (2005) Gender classification in human gait using support vector machine. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 138–145

  39. Yu S, Liao R, An W, Chen H, García EB, Huang Y, Poh N (2019) GaitGANv2: invariant gait feature extraction using generative adversarial networks. Pattern Recogn 87:179–189

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the Department of Science and Technology (DST), New Delhi for the financial support extended under the INSPIRE fellowship scheme.

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Authors and Affiliations

  1. Department of Information Technology, Kannur University, Kannur, Kerala, India

    Sugandhi K & Farha Fatina Wahid

  2. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Raju G

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Correspondence to Sugandhi K.

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K, S., Wahid, F.F. & G, R. Statistical features from frame aggregation and differences for human gait recognition. Multimed Tools Appl 80, 18345–18364 (2021). https://doi.org/10.1007/s11042-021-10655-z

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