The Innovative Role of Process Mining in building Face Re-identification Trajectory
Received: 24 November 2023 | Revised: 10 December and 14 December 2023 | Accepted: 16 December 2023 | Online: 8 February 2024
Corresponding author: Amirah Alharbi
Abstract
Face recognition and tracking technology have witnessed significant advancements during the recent decades. These advancements include improved accuracy and speed in identifying and tracking individuals, as well as the ability to recognize faces in various scale and lighting conditions. Leveraging the potential of face recognition and tracking, this article explores the integration of process mining techniques to discover and visualize face’s appearing trajectories in crowd scenarios, aiming to enhance crowd security, surveillance, and personal identification. Notably, existing face recognition tools typically focus on bounding box localization, neglecting the utilization of face coordinates to construct trajectory models upon face re-identification. In this paper, full system architecture for building a trajectory model of re-identified faces in a crowd is proposed. This approach significantly helped in building a large database of visitor faces, and the proposed trajectory model resulted in a high rate of true positive face re-identification.
Keywords:
process mining, face recognition, face detection, crowd management, surveillance, smart city, trackingDownloads
References
Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, "Face Recognition Systems: A Survey," Sensors, vol. 20, no. 2, Jan. 2020, Art. no. 342.
D. V. Becker and H. Rheem, "Searching for a face in the crowd: Pitfalls and unexplored possibilities," Attention, Perception, & Psychophysics, vol. 82, no. 2, pp. 626–636, Feb. 2020.
U. Singh, J.-F. Determe, F. Horlin, and P. De Doncker, "Crowd Monitoring: State-of-the-Art and Future Directions," IETE Technical Review, vol. 38, no. 6, pp. 578–594, Nov. 2021.
W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, "YOLO-face: a real-time face detector," The Visual Computer, vol. 37, no. 4, pp. 805–813, Apr. 2021.
A. Nadeem et al., "Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance," Sensors, vol. 22, no. 14, Jan. 2022, Art. no. 5270.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, "Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments," presented at the Workshop on Faces in "Real-Life" Images: Detection, Alignment, and Recognition, Marseille, France, Oct. 2008.
M. Wang and W. Deng, "Deep face recognition: A survey," Neurocomputing, vol. 429, pp. 215–244, Mar. 2021.
Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020.
M. S. Zitouni, H. Bhaskar, J. Dias, and M. E. Al-Mualla, "Advances and trends in visual crowd analysis: A systematic survey and evaluation of crowd modelling techniques," Neurocomputing, vol. 186, pp. 139–159, Apr. 2016.
A. Owaidah, D. Olaru, M. Bennamoun, F. Sohel, and N. Khan, "Review of Modelling and Simulating Crowds at Mass Gathering Events: Hajj as a Case Study," Journal of Artificial Societies and Social Simulation, vol. 22, no. 2, 2019, Art. no. 9.
A. B. Altamimi and H. Ullah, "Panic Detection in Crowded Scenes," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5412–5418, Apr. 2020.
C.-T. Lu, C.-J. Ou, and Y.-Y. Lu, "A Practical App for Quickly Calculating the Number of People Using Machine Learning and Convolutional Neural Networks," Applied Sciences, vol. 12, no. 12, Jan. 2022, Art. no. 6239.
L. Wang and A. A. Siddique, "Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology," Measurement and Control, vol. 53, no. 7–8, pp. 1070–1077, Aug. 2020.
Y. Fang, B. Zhan, W. Cai, S. Gao, and B. Hu, "Locality-Constrained Spatial Transformer Network for Video Crowd Counting," in International Conference on Multimedia and Expo, Shanghai, China, Jul. 2019, pp. 814–819.
G. Keith Still, "Crowd Science and Crowd Counting," Impact, vol. 2019, no. 1, pp. 19–23, Jan. 2019.
R. P. Holder and J.-R. Tapamo, "Using Facial Expression Recognition for Crowd Monitoring," in 8th Pacific Rim Symposium on Image and Video Technology, Wuhan, China, Nov. 2017, pp. 463–476.
J. Nelson, "Access control and biometrics," in Handbook of Loss Prevention and Crime Prevention, 6th ed., L. J. Fennelly, Ed. Oxford, UK: Butterworth-Heinemann, 2020, pp. 239–249.
K. B. S. Reddy, O. Loke, S. Jani, and K. Dabre, "Tracking People In Real Time Video Footage Using Facial Recognition," in International Conference on Smart City and Emerging Technology, Mumbai, India, Jan. 2018, pp. 1–6.
P. Bergmann, T. Meinhardt, and L. Leal-Taixe, "Tracking Without Bells and Whistles," in International Conference on Computer Vision, Seoul, South Korea, Nov. 2019, pp. 941–951.
M. Smith and S. Miller, "The ethical application of biometric facial recognition technology," AI & Society, vol. 37, no. 1, pp. 167–175, Mar. 2022.
M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe, "Privacy Preserving Face Recognition Utilizing Differential Privacy," Computers & Security, vol. 97, Oct. 2020, Art. no. 101951.
A. K. Chaitanya, C. H. Kartheek, and D. Nandan, "Study on Real-Time Face Recognition and Tracking for Criminal Revealing," in Soft Computing: Theories and Applications, M. Pant, T. Kumar Sharma, R. Arya, B. C. Sahana, and H. Zolfagharinia, Eds. New York, NY, USA: Springer, 2020, pp. 849–857.
Y. Shi et al., "‘One-Time Face Recognition System’ Drives Changes in Civil Aviation Smart Security Screening Mode," in China’s e-Science Blue Book 2020, Singapore, Singapore: Springer, 2021, pp. 399–425.
J. Chen, W. Su, and Z. Wang, "Crowd counting with crowd attention convolutional neural network," Neurocomputing, vol. 382, pp. 210–220, Mar. 2020.
G. Gao, J. Gao, Q. Liu, Q. Wang, and Y. Wang, "CNN-based Density Estimation and Crowd Counting: A Survey." arXiv, Mar. 28, 2020.
O. Elharrouss, N. Almaadeed, S. Al-Maadeed, and F. Khelifi, "Pose-invariant face recognition with multitask cascade networks," Neural Computing and Applications, vol. 34, no. 8, pp. 6039–6052, Apr. 2022.
W. van der Aalst, "Process Mining: Overview and Opportunities," ACM Transactions on Management Information Systems, vol. 3, no. 2, Apr. 2012, Art. no. 7.
A. B. Lashram, L. Hsairi, and H. A. Ahmadi, "HCLPars: Α New Hierarchical Clustering Log Parsing Method," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11130–11138, Aug. 2023.
"The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research - University President Office | Umm Al-Qura University." https://uqu.edu.sa/en/hajj.
S. I. Serengil and A. Ozpinar, "LightFace: A Hybrid Deep Face Recognition Framework," in Innovations in Intelligent Systems and Applications Conference, Istanbul, Turkey, Oct. 2020, pp. 1–5.
M. O. Oloyede, G. P. Hancke, and H. C. Myburgh, "A review on face recognition systems: recent approaches and challenges," Multimedia Tools and Applications, vol. 79, no. 37, pp. 27891–27922, Oct. 2020.
C. Li, R. Wang, J. Li, and L. Fei, "Face Detection Based on YOLOv3," in Recent Trends in Intelligent Computing, Communication and Devices, V. Jain, S. Patnaik, F. Popențiu Vlădicescu, and I. K. Sethi, Eds. New York, NY, USA: Springer, 2020, pp. 277–284.
J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, "RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild," in IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, Jun. 2020, pp. 5202–5211.
S. Yang, P. Luo, C. C. Loy, and X. Tang, "WIDER FACE: A Face Detection Benchmark," in IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, Jun. 2016, pp. 5525–5533.
Y. Xiong, K. Zhu, D. Lin, and X. Tang, "Recognize complex events from static images by fusing deep channels," in IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, Jun. 2015, pp. 1600–1609.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep Face Recognition," in Procedings of the British Machine Vision Conference 2015, Swansea, UK, 2015, pp. 41.1-41.12.
K. Dharavath, F. A. Talukdar, and R. H. Laskar, "Improving Face Recognition Rate with Image Preprocessing," Indian Journal of Science and Technology, vol. 7, no. 8, pp. 1170–1175, Aug. 2014.
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