Abstract
Algorithms based on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis) are among the most popular appearance-based approaches in face recognition. PCA is recognized as an optimal method to perform dimension reduction, yet being claimed as lacking discrimination ability. LDA once proposed to obtain better classification by using class information. Disputes over the comparison of PCA and LDA have motivated us to study their performance. In this paper, we describe both of these statistical subspace methods and evaluated them using The Database of Faces which comprises 40 subjects with 10 images each. Both identification and verfication results have revealed the superiority of LDA over PCA for this medium-sized database.