A Research on Two-Stage Facial Occlusion Recognition Algorithm based on CNN
Received: 14 August 2024 | Revised: 8 October 2024 | Accepted: 11 October 2024 | Online: 2 December 2024
Corresponding author: Malathy Batumalay
Abstract
In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.
Keywords:
face detection, two-stage, CNN, occlusion recognition, process innovationDownloads
References
A. M. Al-Ghaili et al., "A Review: Image Processing Techniques’ Roles towards Energy-Efficient and Secure IoT," Applied Sciences, vol. 13, no. 4, Jan. 2023, Art. no. 2098.
A. Halder, S. Gharami, P. Sadhu, P. K. Singh, M. Woźniak, and M. F. Ijaz, "Implementing vision transformer for classifying 2D biomedical images," Scientific Reports, vol. 14, no. 1, May 2024, Art. no. 12567.
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
B. Ye et. al., "Small Target Detection Method Based on Morphology Top-Hat Operator," Journal of Image and Graphics, 2002.
L. Chang et al., "Convolutional neural networks in image understanding," Acta Automatica Sinica, vol. 42, no. 9, pp. 1300–1312, Sep. 2016.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, Jun. 2014, pp. 1701–1708.
Y. Sun, X. Wang, and X. Tang, "Deeply learned face representations are sparse, selective, and robust," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 2892–2900.
Y. Sun, D. Liang, X. Wang, and X. Tang, "DeepID3: Face Recognition with Very Deep Neural Networks." arXiv, Feb. 03, 2015.
O. Parkhi, A. Vedaldi, and A. Zisserman, "Deep face recognition," in BMVC 2015 - Proceedings of the British Machine Vision Conference 2015, 2015.
F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 815–823.
V. Blanz and T. Vetter, "A Morphable Model For The Synthesis Of 3D Faces," in Seminal Graphics Papers: Pushing the Boundaries, Volume 2, 1st ed., M. C. Whitton, Ed. New York, NY, USA: ACM, 2023, pp. 157–164.
A. Bansal, A. Nanduri, C. D. Castillo, R. Ranjan, and R. Chellappa, "UMDFaces: An annotated face dataset for training deep networks," in 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, Oct. 2017, pp. 464–473.
A. S. Jackson, A. Bulat, V. Argyriou, and G. Tzimiropoulos, "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression," in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 2017, pp. 1031–1039.
Xiangyu Zhu, Junjie Yan, Dong Yi, Zhen Lei, and S. Z. Li, "Discriminative 3D morphable model fitting," in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia May 2015, pp. 1–8.
Y. Liu, A. Jourabloo, W. Ren, and X. Liu, "Dense Face Alignment," in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy, Oct. 2017, pp. 1619–1628.
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Copyright (c) 2024 Wang Zhe, Malathy Batumalay, Rajermani Thinakaran, Choon Kit Chan, Goh Khang Wen, Zhang Jing Yu, Li Jian Wei, Jeyagopi Raman
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