2D face recognition using PCA and triplet similarity embedding
Bazatbekov B. Turan C. Kadyrov S. Aitimov A.
February 2023Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2023#12Issue 1580 - 586 pp.
The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it.
Face recognition , K-nearest neighbor , Principal component analysis TP , Stochastic gradient descent , Triplet loss
Text of the article Перейти на текст статьи
Department of Computer Science, Faculty of Engineering and Natural Science, Suleyman Demirel University, Kaskelen, Kazakhstan
Department of Mathematics, Faculty of Engineering and Natural Science, Suleyman Demirel University, Kaskelen, Kazakhstan
Department of Computer Science
Department of Mathematics
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026