Baseline Evaluations on the CAS-PEAL-R1 Face Database

In this paper, three baseline face recognition algorithms are evaluated on the CAS-PEAL-R1 face database which is publicly released from a large-scale Chinese face database: CAS-PEAL. The main objectives of the baseline evaluations are to 1) elementarily assess the difficulty of the database for face recognition algorithms, 2) provide an example evaluation protocol on the database, and 3) identify the strengths and weakness of some popular algorithms. Particular description of the datasets used in the evaluations and the underlying philosophy are given. The three baseline algorithms evaluated are Principle Components Analysis (PCA), a combined Principle Component Analysis and Linear Discriminant Analysis (PCA+LDA), and PCA+LDA algorithm based on Gabor features (G PCA+LDA). Four face image preprocessing methods are also tested to emphasize the influences of the preprocessing methods on the performances of face recognition algorithms.