IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
Improving Face Image Representation Using Tangent Vectors and the L1 Norm
Zhicheng LUZhizheng LIANGLei ZHANGJin LIUYong ZHOU
Author information
JOURNAL RESTRICTED ACCESS

2016 Volume E99.A Issue 11 Pages 2099-2103

Details
Abstract

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.

Content from these authors
© 2016 The Institute of Electronics, Information and Communication Engineers
Previous article Next article
feedback
Top