Label Propagation Algorithm for Face Clustering using Shared Nearest Neighbor Similarity

• Published in: Engineering, Technology and Applied Science Research
• Main Author: Yousheng G.; Hamzah R.; Rahim S.K.N.A.; Aminuddin R.; Ang L.
• Format: Article
• Language: English
• Published: Dr D. Pylarinos 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211483283&doi=10.48084%2fetasr.8618&partnerID=40&md5=5264350b39b8f1acc85faebb89564aed

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. © by the authors.