2D face recognition using PCA and triplet similarity embedding

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 similarity metric at the different stages processes. main idea use PCA for feature extraction dimensionality reduction, then train triplet embedding accommodate changes in facial poses, finally orthogonal projection classification. We open source ORL dataset conduct experiments find rates proposed compare them performance one very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that model outperforms kNN. Moreover, when training set smaller than test set, contribution during phase becomes more visible compared without it