Affiliations: [a] Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, Guangdong, China | [b] Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China | [c] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
Abstract: In the field of face recognition, conventional dictionary learning
algorithms mainly focus on reconstructing the training samples and cannot
directly associate the learning procedure with the test samples. Thus, they
may not well represent the test samples and obtain unsatisfactory
classification performance. In addition, though different training samples
have various contributions to learn a dictionary, conventional dictionary
learning algorithms cannot well exploit these contributions. In order to
address these problems, we present a test sample oriented two-phase
dictionary learning (TSOTP-DL) algorithm for face recognition. In the first
phase of the TSOTP-DL algorithm, we use all training samples to provide a
linear representation of the test sample, and select K ``important''
training samples by using the variety of contributions. In the second phase
of the TSOTP-DL algorithm, a dictionary is learned for the test sample by
using the selected K$ ``important'' training samples. The TSOTP-DL algorithm
utilizes the testing sample to select a subset of the training samples for
learning a dictionary, which can reduce the influence of noise. Thus, the
training samples are refined according to their contributions to the test
sample in our algorithm, and it can improve the discriminative ability of
the learned dictionary. In order to further improve the discriminative
ability of the learned dictionary, a label embedding of atoms is constructed
to encourage the same class training samples to have more similar coding
coefficients than different classes. Experiment results demonstrate that our
proposed algorithm achieves better classification results than some
state-of-the-art dictionary learning and sparse coding algorithms on four
public face databases.
Keywords: Dictionary learning, sparse coding, face recognition
