Label Propagation Algorithm for Face Clustering using Shared Nearest Neighbor Similarity
Received: 2 September 2024 | Revised: 3 October 2024 and 11 October 2024 | Accepted: 13 October 2024 | Online: 2 December 2024
Corresponding author: Raihah Aminuddin
Abstract
Facial image datasets are particularly vulnerable to challenges such as lighting variations and occlusion, which can complicate data classification. Semi-supervised learning, using a limited amount of labeled facial data, offers a solution by enhancing face classification accuracy while reducing manual labeling efforts. The Label Propagation Algorithm (LPA) is a commonly used semi-supervised algorithm that employs Radial Basis Function (RBF) to measure similarities between data nodes. However, RBF struggles to capture complex nonlinear relationships in facial data. To address this, an improved LPA is proposed that integrates Shared Nearest Neighbor (SNN) to enhance the correlation measurement between facial data and RBF. Three known datasets were considered: FERET, Yale, and ORL. The experiments showed that in the case of insufficient label samples, the accuracy reached 89.76%, 92.46%, and 81.48%, respectively. The proposed LPA enhances clustering robustness by introducing 128 dimensional facial features and more complex similarity measurement. The parameter of similarity measurement can be adjusted based on the characteristics of different datasets to achieve better clustering results. The improved LPA achieved better performance and face clustering effectiveness by enhancing robustness and adaptability.
Keywords:
machine learning, label propagation algorithm, k-means, pairwise constraints, shared nearest neighborDownloads
References
G. Lou and H. Shi, "Face image recognition based on convolutional neural network," China Communications, vol. 17, no. 2, pp. 117–124, Feb. 2020.
N. F. Greenwald et al., "Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning," Nature Biotechnology, vol. 40, no. 4, pp. 555–565, Apr. 2022.
X. Yang, Z. Song, I. King, and Z. Xu, "A Survey on Deep Semi-Supervised Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 8934–8954, Sep. 2023.
W. Liu, H. Wang, X. Shen, and I. W. Tsang, "The Emerging Trends of Multi-Label Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7955–7974, Aug. 2022.
X. Wang, L. Lian, and S. X. Yu, "Unsupervised Selective Labeling for More Effective Semi-supervised Learning," in European Conference on Computer Vision, Tel Aviv, Israel, Oct. 2022, pp. 427–445.
P. Cascante-Bonilla, F. Tan, Y. Qi, and V. Ordonez, "Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6912–6920, May 2021.
M. Gao, Z. Zhang, G. Yu, S. Ö. Arık, L. S. Davis, and T. Pfister, "Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost," in European Conference on Computer Vision, Glasgow, United Kingdom, Aug. 2020, pp. 510–526.
S. Calderon-Ramirez et al., "Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images," in 25th International Conference on Pattern Recognition, Milan, Italy, Jan. 2021, pp. 5294–5301.
M. Han, H. Wu, Z. Chen, M. Li, and X. Zhang, "A survey of multi-label classification based on supervised and semi-supervised learning," International Journal of Machine Learning and Cybernetics, vol. 14, no. 3, pp. 697–724, Mar. 2023.
N. Mamat, M. F. Othman, R. Abdulghafor, A. A. Alwan, and Y. Gulzar, "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, vol. 15, no. 2, Jan. 2023, Art. no. 901.
Q. Wang, C. Wang, H. Tang, D. Wu, and F. Wang, "Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios," Stochastic Environmental Research and Risk Assessment, vol. 38, no. 7, pp. 2875–2890, Jul. 2024.
M. Baradaran and R. Bergevin, "A critical study on the recent deep learning based semi-supervised video anomaly detection methods," Multimedia Tools and Applications, vol. 83, no. 9, pp. 27761–27807, Mar. 2024.
C. Chen et al., "Interactive Graph Construction for Graph-Based Semi-Supervised Learning," IEEE transactions on visualization and computer graphics, vol. 27, no. 9, pp. 3701–3716, Sep. 2021.
T. Fan, Z. Yao, L. Han, B. Liu, and L. Lv, "Density peaks clustering based on k-nearest neighbors sharing," Concurrency and Computation: Practice and Experience, vol. 33, no. 5, 2021, Art. no. e5993.
Q. Liu, J. Yang, M. Deng, C. Song, and W. Liu, "SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows," International Journal of Geographical Information Science, vol. 36, no. 2, pp. 253–279, Feb. 2022.
L. Sun, X. Qin, W. Ding, J. Xu, and S. Zhang, "Density peaks clustering based on k-nearest neighbors and self-recommendation," International Journal of Machine Learning and Cybernetics, vol. 12, no. 7, pp. 1913–1938, Jul. 2021.
M. Yu and R. Cui, "Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design," Mobile Information Systems, vol. 2021, no. 1, 2021, Art. no. 3790413.
D. Hu, K. Liang, S. Zhou, W. Tu, M. Liu, and X. Liu, "scDFC: A deep fusion clustering method for single-cell RNA-seq data," Briefings in Bioinformatics, vol. 24, no. 4, Jul. 2023, Art. no. bbad216.
P. Kumar and S. L. Ta, "Face Recognition Attendance System Using Local Binary Pattern Algorithm," in 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Vellore, India, Dec. 2023, pp. 1–6.
A. B. S. Salamh and H. I. Akyüz, "A Novel Feature Extraction Descriptor for Face Recognition," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8033–8038, Feb. 2022.
S. Naseem, S. S. Rathore, S. Kumar, S. Gangopadhyay, and A. Jain, "An approach to occluded face recognition based on dynamic image-to-class warping using structural similarity index," Applied Intelligence, vol. 53, no. 23, pp. 28501–28519, Dec. 2023.
Z. Song, X. Yang, Z. Xu, and I. King, "Graph-Based Semi-Supervised Learning: A Comprehensive Review," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8174–8194, Aug. 2023.
J. Wang, Y. Guo, X. Wen, Z. Wang, Z. Li, and M. Tang, "Improving graph-based label propagation algorithm with group partition for fraud detection," Applied Intelligence, vol. 50, no. 10, pp. 3291–3300, Oct. 2020.
C. Zhang, T. Bai, and B. Wu, "Semi-supervised Graph Learning with Few Labeled Nodes," in International Conference on Database Systems for Advanced Applications, Apr. 2022, pp. 423–438.
N. Reyaz, G. Ahamad, N. J. Khan, M. Naseem, and J. Ali, "SVMCTI: support vector machine based cricket talent ıdentification model," International Journal of Information Technology, vol. 16, no. 3, pp. 1931–1944, Mar. 2024.
J. Wang and Y. Dong, "Measurement of Text Similarity: A Survey," Information, vol. 11, no. 9, Sep. 2020, Art. no. 421.
A. D. Sokolova and A. V. Savchenko, "Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features," Optical Memory and Neural Networks, vol. 29, no. 1, pp. 19–29, Jan. 2020.
D. E. King, "Dlib-ml: A Machine Learning Toolkit," The Journal of Machine Learning Research, vol. 10, pp. 1755–1758, Sep. 2009.
W. S. Ow, M. A. Ilyas, N. H. Kamarudin, M. B. Othman, Z. B. Zulkoffli, and Y. B. Chu, "Face Recognition Authentication System with CNN and Blink Detection Algorithm," in International Conference on Computing, Control and Industrial Engineering, Hangzhou, China, Feb. 2023, pp. 491–501.
"make_circles," scikit-learn. https://scikit-learn/stable/modules/generated/sklearn.datasets.make_circles.html.
"The Database of Faces." 2001, [Online]. Available: https://cam-orl.co.uk/facedatabase.html.
"Yale Face Database." 1997, [Online]. Available: http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html.
"color FERET Database." NIST, Jan. 31, 2011, [Online]. Available: https://www.nist.gov/itl/products-and-services/color-feret-database.
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Copyright (c) 2024 Gao Yousheng, Raseeda Hamzah, Siti Khatijah Nor Abdul Rahim, Raihah Aminuddin, Ang Li
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