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Gait Recognition with Various Data Modalities: A Review

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Biometric Recognition (CCBR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13628))

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Abstract

Gait recognition aims to recognize one subject by the way she/he walks without alerting the subject, which has drawn increasing attention. Recently, gait recognition can be represented using various data modalities, such as RGB, skeleton, depth, infrared data, acceleration, gyroscope, .etc., which have various advantages depending on the application scenarios. In this paper, we present a comprehensive survey of recent progress in gait recognition methods based on the type of input data modality. Specifically, we review commonly-used gait datasets with different gait data modalities, following with effective gait recognition methods both for single data modality and multiple data modalities. We also present comparative results of effective gait recognition approaches, together with insightful observations and discussions.

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References

  1. Alobaidi, H., Clarke, N., Li, F., Alruban, A.: Real-world smartphone-based gait recognition. Comput. Secur. 113, 102557 (2022)

    Article  Google Scholar 

  2. An, W., et al.: Performance evaluation of model-based gait on multi-view very large population database with pose sequences. IEEE Trans. Biometrics Behav. Ident. Sci. 2(4), 421–430 (2020)

    Article  Google Scholar 

  3. Castro, F.M., Marín-Jiménez, M., Mata, N.G., Muñoz-Salinas, R.: Fisher motion descriptor for multiview gait recognition. Int. J. Pattern Recogn. Artif. Intell. 31(01), 1756002 (2017)

    Article  Google Scholar 

  4. Chao, H., He, Y., Zhang, J., Feng, J.: Gaitset: regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8126–8133 (2019)

    Google Scholar 

  5. Choi, S., Kim, J., Kim, W., Kim, C.: Skeleton-based gait recognition via robust frame-level matching. IEEE Trans. Inf. Forensics Secur. 14(10), 2577–2592 (2019)

    Article  Google Scholar 

  6. Fan, C., et al.: Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225–14233 (2020)

    Google Scholar 

  7. Gafurov, D., Snekkenes, E.: Gait recognition using wearable motion recording sensors. EURASIP J. Adv. Signal Process. 2009, 1–16 (2009)

    Article  MATH  Google Scholar 

  8. Gross, R., Shi, J.: The CMU motion of body (MOBO) database (2001)

    Google Scholar 

  9. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2005)

    Article  Google Scholar 

  10. Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The tum gait from audio, image and depth (gaid) database: Multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25(1), 195–206 (2014)

    Article  Google Scholar 

  11. Hossain, M.A., Makihara, Y., Wang, J., Yagi, Y.: Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn. 43(6), 2281–2291 (2010)

    Article  Google Scholar 

  12. Huang, Z., et al.: 3D local convolutional neural networks for gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14920–14929 (2021)

    Google Scholar 

  13. Iwama, H., Okumura, M., Makihara, Y., Yagi, Y.: The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans. Inf. Forensics Secur. 7(5), 1511–1521 (2012)

    Article  Google Scholar 

  14. John, V., Englebienne, G., Krose, B.: Person re-identification using height-based gait in colour depth camera. In: 2013 IEEE International Conference on Image Processing, pp. 3345–3349. IEEE (2013)

    Google Scholar 

  15. Juefei-Xu, F., Bhagavatula, C., Jaech, A., Prasad, U., Savvides, M.: Gait-id on the move: pace independent human identification using cell phone accelerometer dynamics. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 8–15. IEEE (2012)

    Google Scholar 

  16. Li, W., Kuo, C.-C.J., Peng, J.: Gait recognition via GEI subspace projections and collaborative representation classification. Neurocomputing 275, 1932–1945 (2018)

    Article  Google Scholar 

  17. Liao, R., Cao, C., Garcia, E.B., Yu, S., Huang, Y.: Pose-based temporal-spatial network (ptsn) for gait recognition with carrying and clothing variations. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 474–483. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_51

    Chapter  Google Scholar 

  18. Liao, R., Shiqi, Yu., An, W., Huang, Y.: A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 98, 107069 (2020)

    Article  Google Scholar 

  19. Lin, B., Zhang, S., Yu, X.: Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14648–14656 (2021)

    Google Scholar 

  20. Jiwen, L., Wang, G., Moulin, P.: Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Trans. Inf. Forensics Secur. 9(1), 51–61 (2013)

    Google Scholar 

  21. Makihara, Y., Mannami, H., Yagi, Y.: Gait analysis of gender and age using a large-scale multi-view gait database. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6493, pp. 440–451. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19309-5_34

    Chapter  Google Scholar 

  22. Middleton, L., Buss, A.A., Bazin, A., Nixon, M.S.: A floor sensor system for gait recognition. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2005), pp. 171–176. IEEE (2005)

    Google Scholar 

  23. Rida, I., Almaadeed, N., Almaadeed, S.: Robust gait recognition: a comprehensive survey. IET Biometrics 8(1), 14–28 (2019)

    Article  Google Scholar 

  24. Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)

    Article  Google Scholar 

  25. Shen, C., Yu, S., Wang, J., Huang, G.Q., Wang, L.: A comprehensive survey on deep gait recognition: algorithms, datasets and challenges. arXiv preprint arXiv:2206.13732 (2022)

  26. Shutler, J.D., Grant, M.G., Nixon, M.S., Carter, J.N.: On a large sequence-based human gait database. In: Lotfi, A., Garibaldi, J.M. (eds.) Applications and Science in Soft Computing, pp. 339–346. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-45240-9_46

  27. Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-based gait recognition: a survey. IEEE Access 6, 70497–70527 (2018)

    Article  Google Scholar 

  28. Sprager, S., Juric, M.B.: An efficient hos-based gait authentication of accelerometer data. IEEE Trans. Inf. Forensics Secur. 10(7), 1486–1498 (2015)

    Article  Google Scholar 

  29. Sun, J., Wang, Y., Li, J., Wan, W., Cheng, D., Zhang, H.: View-invariant gait recognition based on kinect skeleton feature. Multimedia Tools Appl. 77(19), 24909–24935 (2018). https://doi.org/10.1007/s11042-018-5722-1

    Article  Google Scholar 

  30. Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(1), 1–14 (2018). https://doi.org/10.1186/s41074-018-0039-6

    Article  Google Scholar 

  31. Tan, D., Huang, K., Yu, S., Tan, T.: Efficient night gait recognition based on template matching. In 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pages 1000–1003. IEEE (2006)

    Google Scholar 

  32. Teepe, T., Khan, A., Gilg, J., Herzog, F., Hörmann, S., Rigoll, G.: Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2314–2318. IEEE (2021)

    Google Scholar 

  33. Trung, N.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: Performance evaluation of gait recognition using the largest inertial sensor-based gait database. In 2012 5th IAPR International Conference on Biometrics (ICB), pp. 360–366. IEEE (2012)

    Google Scholar 

  34. Tsuji, A., Makihara, Y., Yagi, Y.: Silhouette transformation based on walking speed for gait identification. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 717–722. IEEE (2010)

    Google Scholar 

  35. Uddin, M.Z., et al.: The OU-ISIR large population gait database with real-life carried object and its performance evaluation. IPSJ Trans. Comput. Vis. Appl. 10(1), 1–11 (2018)

    Google Scholar 

  36. Wang, L., Tan, T., Ning, H., Weiming, H.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)

    Article  Google Scholar 

  37. Zifeng, W., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 209–226 (2016)

    Google Scholar 

  38. Chi, X., Makihara, Y., Ogi, G., Li, X., Yagi, Y., Jianfeng, L.: The OU-ISIR gait database comprising the large population dataset with age and performance evaluation of age estimation. IPSJ Trans. Comput. Vis. Appl. 9(1), 1–14 (2017)

    Google Scholar 

  39. Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 4, pp. 441–444. IEEE (2006)

    Google Scholar 

  40. Yunas, S.U., Alharthi, A.,Ozanyan, K.B .: Multi-modality sensor fusion for gait classification using deep learning. In: 2020 IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2020)

    Google Scholar 

  41. Zhang, Y., Huang, Y., Shiqi, Yu., Wang, L.: Cross-view gait recognition by discriminative feature learning. IEEE Trans. Image Process. 29, 1001–1015 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  42. Zhang, Y., Pan, G., Jia, K., Minlong, L., Wang, Y., Zhaohui, W.: Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Trans. Cybern. 45(9), 1864–1875 (2014)

    Article  Google Scholar 

  43. Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4710–4719 (2019)

    Google Scholar 

  44. Zheng, J., Liu, X., Liu, W., He, L., Yan, C., Mei,T.: Gait recognition in the wild with dense 3D representations and a benchmark. arXiv preprint arXiv:2204.02569, 2022

  45. Zhu, Z.., et al.. Gait recognition in the wild: a benchmark. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14789–14799 (2021)

    Google Scholar 

  46. Zou, Q., Ni, L., Wang, Q., Li, Q., Wang, S.: Robust gait recognition by integrating inertial and RGBD sensors. IEEE Trans. Cybernet. 48(4), 1136–1150 (2017)

    Article  Google Scholar 

  47. Zou, Q., Wang, Y., Wang, Q., Zhao, Y., Li, Q.: Deep learning-based gait recognition using smartphones in the wild. IEEE Trans. Inf. Forensics Secur. 15, 3197–3212 (2020)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. IoT Research Center, China Electronics Standardization Institute, NO.1 Andingmen East Street, Beijing, China

    Wei Li, Jiwei Song, Yao Liu, Chen Zhong, Li Geng & Wenfeng Wang

Authors
  1. Wei Li
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  2. Jiwei Song
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  3. Yao Liu
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  4. Chen Zhong
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  5. Li Geng
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  6. Wenfeng Wang
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Corresponding author

Correspondence to Jiwei Song .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Weihong Deng

  2. Tsinghua University, Beijing, China

    Jianjiang Feng

  3. Beihang University, Beijing, China

    Di Huang

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Meina Kan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  6. Tsinghua University, Beijing, China

    Fang Zheng

  7. China Electronics Standardization Institute, Beijing, China

    Wenfeng Wang

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

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Li, W., Song, J., Liu, Y., Zhong, C., Geng, L., Wang, W. (2022). Gait Recognition with Various Data Modalities: A Review. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_42

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  • DOI: https://doi.org/10.1007/978-3-031-20233-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20232-2

  • Online ISBN: 978-3-031-20233-9

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