Locally principal component learning for face representation and recognition

摘要

This paper develops a method called locally principal component analysis (LPCA) for data representation. LPCA is a linear and unsupervised subspace-learning technique, which focuses on the data points within local neighborhoods and seeks to discover the local structure of data. This local structure may contain useful information for discrimination. LPCA is tested and evaluated using the AT&T face database. The experimental results show that LPCA is effective for dimension reduction and more powerful than PCA for face recognition.