自動辨識人的身分是一項經典但又具挑戰性的問題，且其應用遍佈在人們的生活周遭，像是以自動提款機存取個人帳戶時就必須要通過身分驗證。辨識身分比較傳統的方法包括比對身分證件或是使用帳號密碼等，而近年來逐漸發展出許多生物特徵的辨識技術，人們便不需額外攜帶證件或煩惱帳號密碼被遺忘的問題，諸如指紋辨識或是虹膜辨識等系統越來越普遍且也有不錯效果，然而在大多數情況下使用者必須主動提供生物特徵給系統辨識。或許在某些像是非重要設施的情況下，可以不必讓使用者主動提供其生物特徵，而是被動地讓系統直接取得並做簡單的辨識，這樣一來就能增加使用者的便利性，這樣能夠簡單取得但不具有那麼高獨特性的生物特徵就稱為軟性生物特徵。在本研究中，我們針對人們的步伐特徵進行擷取、處理與分析等工作，此一步伐分析能夠在一定距離外即可完成，在不影響使用者原本活動的情況下就能完成辨識。明確地而言，我們利用深度攝影機來擷取使用者的動作片段，並藉由資料前處理與分群技術將這些體感資料轉換成一連串的動作字串，接著利用資料檢索技術便可找出能夠代表個人習慣動作的動作字串，以進而辨識待測者的身分。最後，我們使用實際錄製的體感資料來進行實驗評估，結果顯示使用此方法對於步伐辨識具有相當的鑑別度。

Identifying a specific user is an old but challenging problem, and its applications are ubiquitous in our daily lives. For example, we have to prove our identity to gain access to a bank account when using the cash machine. Conventional person identification methods are using an ID card or the combination of a username and password. Recently, new techniques based on biometrics have been introduced so that people do not need to worry if they forget their username and password. For example, fingerprint and iris recognition are becoming common methods of person identification; however, users are usually required to interact with a system to use these traits. In some non-critical situations, it may be more convenient to utilize soft biometrics for person identification, although these features are not as unique for a specific person. In this work, we propose to conduct gait analysis that can be performed from a distance without disturbing user activities. We utilize depth cameras to capture user movements and create motion sequences. Then, a motion sequence is transformed to a motion string with appropriate data preprocessing and clustering techniques. Representative motion strings representing the individual behaviour of a user are retrieved and utilized to identify people. Empirical studies based on real motion data show that our approach performs well in person identification.

[1] M. Alvaro, G. Begonya, and M. Amaia, “Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications,” Sensors, 14(2): 3362-3394, February 2014.
[2] Ball, D. Rye, F. Ramos, and M. Velonaki, “Unsupervised Clustering of People from ‘Skeleton’ Data,” Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction, pp.225-226, March 2012.
[3] J. Bernard, N. Wilhelm, B. Kruger, T. May, T. Schreck, and J. Kohlhammer, “MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation,” IEEE Transactions on Visualization and Computer Graphics, 19(12):2257-2266, September 2013.
[4] J. E. Boyd, and J. J. Little, “Biometric Gait Recognition,” Advanced Studies in Biometrics, pp.19-42, 2005.
[5] S. Celebi, A. S. Aydin, T. T. Temiz, and T. Arici, “Gesture Recognition Using Skeleton Data with Weighted Dynamic Time Warping,” Proceedings of the 8th International Conference on Computer Vision Theory and Applications, February 2013.
[6] T. Cloete and C. Scheffer, “Repeatability of an Off-the-shelf, Full Body Inertial Motion Capture System during Clinical Gait Analysis,” Proceedings of the 32nd Annual International Conference on IEEE Engineering in Medicine and Biology Society, pp. 5125-5128, August 2010.
[7] A. Dantcheva, C. Velardo, A. D’Angelo, and J.-L. Dugelay, “Bag of Soft Biometrics for Person Identification,” Multimedia Tools and Applications, 51(2):739-777, January 2011.
[8] J. Gu, S. Wang, "Action and Gait Recognition From Recovered 3-D Human Joints," IEEE Transactions on Systems, Man, and Cybernetics Society, 40(4): 1021-1033, August 2010.
[9] X. Guo and Q. Zhang, “3D Human Motion Retrieval Based on Human Hierarchical Index Structure,” Biology of Sport, 30(2):145, March 2013.
[10] S. Hamano, W. Takano, and Y. Nakamura, “Motion Data Retrieval Based on Statistic Correlation between Motion Symbol Space and Language,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3401-3406, September 2011.
[11] J. Han and B. Bhanu, “Individual Recognition Using Gait Energy Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 316-322, 2006.
[12] W. He, and P. Li, “Gait Recognition Using the Temporal Information of Leg Angles,” Proceedings of the 2010 3rd IEEE International Conference on Computer Science and Information Technology, pp. 78-83, July 2010.
[13] Y. Hu, S. Wu, S. Xia, J. Fu, and W. Chen, “Motion Track: Visualizing Variations of Human Motion Data,” Proceedings of the 3rd IEEE Pacific Visualization Symposium, pp. 153-160, March 2010.
[14] T. Huang, H. Liu, and G. Ding, “Motion Retrieval Based on Kinetic Features in Large Motion Database,” Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 209-216, October 2012.
[15] A. K. Jain, S. C. Dass, and K. Nandakumar, “Soft Biometric Traits for Personal Recognition Systems,” Biometric Authentication, Vol. 3072, pp. 731-738, July 2004.
[16] A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 4-20, January 2004.
[17] S. Jiang, Y. Wang, Y. Zhang, and J. Sun, “Real Time Gait Recognition System Based on Kinect Skeleton Feature,” Proceedings of the 12th Asian Conference on Computer Vision 2014 Wrokshops, pp. 46-57, November 2014.
[18] V. John, E. Trucco, and S. Ivekovic, “Markerless Human Articulated Tracking Using Hierarchical Particle Swarm Optimisation,” Image and Vision Computing, 28(11):1530-1547, November 2010.
[19] T. Kohonen, “The Self-organizing Map,” Proceedings of IEEE, 78(9):1464-1480, September 1990.
[20] F. A. Kondori, S. Yousefi, H. Li, and S. Sonning, “3D Head Pose Estimation Using The Kinect,” Proceedings of the 2011 International Conference on Wireless Communications and Signal Processing, pp. 1-4, November 2011.
[21] M. Müller, T. Röder and M. Clausen, “Efficient Content-Based Retrieval of Motion Capture Data,” Proceedings of the 32nd International Conference on Computer Graphics and Interactive Techniques, pp. 677–685, July 2005.
[22] M. Müller and T. Röder, “Motion Templates for Automatic Classification and Retrieval of Motion Capture Data,” Proceedings of the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 137-146, September 2006.
[23] B. C. Munsell, A. Temlyakov, C. Qu, and S. Wang, “Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos,” Proceedings of the European Conference on Computer Vision 2012 Workshops and Demonstrations, pp.91-100, October 2012.
[24] S. Prakash, and P. Gupta, “Ear Biometrics in 2D and 3D,” March 2015.
[25] PrimeSense: 3D Sensors and Natural Interaction Solutions, http://www.primesense.com/.
[26] J. Preis, M. Kessel, and M. Werner, “Gait Recognition with Kinect,” Proceedings of the First Workshop on Kinect in Pervasive Computing, pp.1-4, January 2012.
[27] D. A. Reid, S. Samangooei, C. Chen, M. S. Nixon, and A. Ross, “Soft Biometrics for Surveillance: An Overview,” Machine Learning: Theory and Applications, Handbook of Statistics, 31: 327-352, January 2013.
[28] Y. Sakamoto, S. Kuriyama, and T. Kaneko, “Motion Map: Image-based Retrieval And Segmentation of Motion Data,” Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 259–266, August 2004.
[29] J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, “Real-time Human Pose Recognition in Parts from Single Depth Image,” Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297-1304, June 2011.
[30] A. Sinha, K. Chakravarty, and B. Bhowmick, “Person Identification using Skeleton Information from Kinect,” Proceedings of the 6th International Conference on Advances in Computer-Human Interactions, pp.101-108, February 2013.
[31] S. Sivapalan, D. Chen, S. Denman, S. Sridharan, and C. Fookes, “Gait Energy Volumes and Frontal Gait Recognition using Depth Images,” Proceedings of the International Joint Conference on Biometrics, pp.1-6, October 2011.
[32] J. Tang, J. Luo, T. Tjahjadi, and Y. Gao, “2.5D Multi-View Gait Recognition Based on Point Cloud Registration,” Sensors. 2014, 14(4):6124-6143, January 2014.
[33] W.-G. Teng, P.-L. Chang, and C.-T. Yang, “Adaptive and Efficient Colour Quantisation Based on a Growing Self-Organising Map,” IET Image Processing, 6(5):463-472, July 2012.
[34] P. Tome, J. Fierrez, R. V.-Rodriguez, and M. S. Nixon, “Soft biometrics and Their Application in Person Recognition at a Distance,” IEEE Transactions on Information Forensics and Security, (9)3:464-475, March 2014.
[35] J.A. Unar, W. C. Seng, and A. Abbasi, “A Review of Biometric Technology along with Trends and Prospects,” Journal of Pattern Recognition, 47(8):2673-2688, August 2014.
[36] J. Wang, M. She, S. Nahavandi, and A. Kouzani, “A review of vision-based gait recognition methods for human identification,” Proceedings of the Digital Image Computing: Techniques and Applications, 2010 International Conference, pp. 320-327, December 2010.
[37] S. Wu, S. Xia, Z. Wang, and C. Li, “Efficient Motion Data Indexing and Retrieval with Local Similarity Measure of Motion Strings,” The Visual Computer, 25(5-7):499-508, May 2009.
[38] Q. Xiao, Y. Luo, and S. Gao, “Human Motion Retrieval with Symbolic Aggregate Approximation,” Proceedings of the 24th Chinese Control and Decision Conference, pp. 3632-3636, May 2012.
[39] D.-C. Yu and W.-G. Teng, “Indexing and Retrieval of Human Motion Data Based on a Growing Self-Organizing Map,” Proceedings of the 2014 International Conference on Data Science and Advanced Analytics, pages 66-71, Shanghai, China, October 30-November 1, 2014.
[40] V. O. Andersson, and M. A. Ricardo, “Person Identification Using Anthropometric and Gait Data from Kinect Sensor,” Proceedings of the Conference on Association for the Advancement of Artificial Intelligence, pages 425-431, Austin, Texas, USA, January 2015.