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Deep Learning Feature Extraction Architectures for Real-Time Face Detection

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Abstract

A Video Surveillance system can be used for a variety of purposes, including protection, secure data, crowd flux analytics and congestion analysis, individual recognition, anomalous activity detection, and so on. Video Surveillance systems play the key role in the human detection using the face features extraction. It helps in many applications like terrorists attack, thief identifying by detecting the face of the person but mostly failed in real-time aspect. In this context, we propose a method that significantly aids in the extraction and learning of features. To reduce the face recognition error, we use a bounding box regression model. To train the features, we utilized a CNN-based feature learning model with log-likelihood ratio calculations between inter- and intra-features. To increase the quality of video frames, we used a histogram redistribution image enhancement technique. Finally, a Background Subtracted Faster RCNN for video-based face recognition (BSF-RCNN-VFR) is used to discriminate the groups of detected faces. A comprehensive experiment is carried out on their datasets to demonstrate that the proposed solution performs better, and we compared the existing models with proposed models. We achieved 94.2 accuracy percentage. In this paper, the CNN models like AlexNet, ResNet and datasets like UADFV, Celeb-DF, FF++, DFDC, etc., accuracies are compared.

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References

  1. Zhang S, Chi C, Lei Z, Li S Z. Refineface: refinement neural network for high performance face detection. IEEE Trans Pattern Anal Mach Intell. 2020

  2. Li Y, Sun B, Wu T, Wang Y. Face detection with end-to-end integration of a convnet and a 3d model. In: European Conference on computer vision. Cham: Springer; Leibe et al. (Eds.): ECCV 2016, October, pp. 420–36.

  3. Zhang K, Zhang Z, Li Z, Qiao Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett. 2016;23(10):1499–503.

    Article  Google Scholar 

  4. Tao QQ, Zhan S, Li XH, Kurihara T. Robust face detection using local CNN and SVM based on kernel combination. Neurocomputing. 2016;211:98–105.

    Article  Google Scholar 

  5. Pham HX, Pavlovic V, Cai J, Cham TJ. Robust real- time performance-driven 3D face tracking. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE. 2016, December, pp. 1851–56.

  6. Ranganatha S, Gowramma YP. A novel fused algorithm for human face tracking in video sequences. In 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE. 2016, October, pp. 1–6.

  7. Soldić M, Marčetić D, Maračić M, Mihalić D, Ribarić S. Real-time face tracking under long-term full occlusions. In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis. IEEE, 2017, September; pp. 147–152.

  8. Hu X, Chen L, Tang B, Cao D, He H. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mech Syst Signal Process. 2018;100:482–500.

    Article  Google Scholar 

  9. Maleš L, Marčetić D, Ribarić S. A multi-agent dynamic system for robust multi-face tracking. Expert Syst Appl. 2019;126:246–64.

    Article  Google Scholar 

  10. Yuan, S., Yu, X., Majid, A. Robust face tracking using Siamese VGG with pre-training and fine-tuning. In: 2019 4th International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2019, April; pp. 170-74.

  11. Wu B, Hu BG, Ji Q. A coupled hidden Markov random field model for simultaneous face clustering and tracking in videos. Pattern Recogn. 2017;64:361–73.

    Article  Google Scholar 

  12. Congcong Z, Zhenhua Y, Suping W, Hao L. Dual-cycle deep reinforcement learning for stabilizing face tracking. In 2019 IEEE International Conference on Multimedia Expo Workshops (ICMEW). IEEE, 2019, July; pp. 543–48.

  13. Ding C, Tao D. Robust face recognition via multimodal deep face representation. IEEE Trans Multimed. 2015;17(11):2049–58.

    Article  Google Scholar 

  14. Sun Y, Liang D, Wang X, Tang X. Deepid3: face recognition with very deep neural networks. 2015. arXiv preprint arXiv:1502.00873.

  15. Rejeesh MR. Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl. 2019;78(16):22691–710.

    Article  Google Scholar 

  16. Ding C, Choi J, Tao D, Davis LS. Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell. 2015;38(3):518–31.

    Article  Google Scholar 

  17. Ng CJ, Teoh ABJ. DCTNet: A simple learning-free approach for face recognition. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015, December; pp. 761-68.

  18. Gao S, Zhang Y, Jia K, Lu J, Zhang Y. Single sample face recognition via learning deep supervised autoencoders. IEEE Trans Inf Forensics Secur. 2015;10(10):2108–18.

    Article  Google Scholar 

  19. Lei Z, Zhang X, Yang S, Ren Z, Akindipe OF. RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking. Enterp Inform Syst. 2020;14(9–10):1339–79.

    Google Scholar 

  20. Li Y, Xie Y, Lu X. Multi-face recognition and dynamic tracking based on reinforcement learning algorithm. In: MATEC Web of Conferences (Vol. 336, p. 06006). EDP Sciences. 2021.

  21. Ren G, Lu X, Li Y. A cross-camera multi-face tracking system based on double triplet networks. IEEE Access. 2021;9:43759–74.

    Article  Google Scholar 

  22. Pujol FA, Pujol M, Jimeno-Morenilla A, Pujol MJ. Face detection based on skin color segmentation using fuzzy entropy. Entropy. 2017;19(1):26.

    Article  Google Scholar 

  23. Wu X, Zhao J, Wang H. Face segmentation based on leve set and deep learning prior shape. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017; pp. 1-5.

  24. Lin K, Zhao H, Lv J, Zhan J, Liu X, Chen R, Huang Z. Face detection and segmentation with generalized intersection over union based on mask R- CNN. In: International Conference on brain inspired cognitive systems. Springer, Cham, 2019; pp. 106-116.

  25. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2016; pp. 770–78.

  26. Ortiz EG, Becker BC. Face recognition for web-scale datasets. Comput Vis Image Underst. 2014;118:153–70.

    Article  Google Scholar 

  27. Savaliya R, Kalaria V. A Video Surveillance system for traffic application. SIJ Trans Comput Sci En. Appl (CSEA). 2014;2(8).pp 1–5.

  28. https://www.kaggle.com/code/blurredmachine/alexnet-architecture-a-complete-guide. Accessed 20 Sept 2021

  29. https://www.geeksforgeeks.org/vgg-16-cnn-model/. Accessed 12 Jan 2023

  30. https://builtin.com/machine-learning/relu-activation-function. Accessed 7 Feb 2023

  31. Huang J, Wang X, Du B, Du P, Xu C. DeepFake MNIST+: a DeepFake facial animation dataset. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021; pp. 1973-1982. https://doi.org/10.1109/ICCVW54120.2021.00224.

  32. https://paperswithcode.com/datasets. Accessed 12 Feb 2023

  33. He Y, et al. ForgeryNet: a versatile benchmark for comprehensive forgery analysis. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021; pp. 4358–4367. https://doi.org/10.1109/CVPR46437.2021.00434.

  34. https://ngrok.com/. Accessed 20 Dec 2022

  35. Ayache F, Alti A. Performance evaluation of machine learning for recognizing human facial emotions. Revue d’Intell Artif. 2020;34(3):267–75. https://doi.org/10.18280/ria.340304.

    Article  Google Scholar 

  36. Ayeche F, Alti A. HDG and HDGG: an extensible feature extraction descriptor for effective face and facial expressions recognition. Pattern Anal Appl. 2021;24:1095–110. https://doi.org/10.1007/s10044-021-00972-2.

    Article  Google Scholar 

  37. Ayeche F, Alti A. Local directional gradients extension for recognising face and facial expressions. Int J Intell Syst Technol Appl. 2022;20(6):487–509. https://doi.org/10.1504/ijista.2022.128525

  38. Ayeche F, Alti A. Novel descriptors for effective recognition of face and facial expressions. Revue d’Intell Artif. 2020;34(5):521–30. https://doi.org/10.18280/ria.340501.

    Article  Google Scholar 

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Correspondence to Srikanth Bethu.

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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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B, R.T., D, M., Duvva, L. et al. Deep Learning Feature Extraction Architectures for Real-Time Face Detection. SN COMPUT. SCI. 4, 645 (2023). https://doi.org/10.1007/s42979-023-02023-5

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