Share Email Print

Journal of Electronic Imaging

Cascaded K-means convolutional feature learner and its application to face recognition
Author(s): Daoxiang Zhou; Dan Yang; Xiaohong Zhang; Sheng Huang; Shu Feng
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

Paper Details

Date Published: 1 September 2017
PDF: 13 pages
J. Electron. Imag. 26(5) 053001 doi: 10.1117/1.JEI.26.5.053001
Published in: Journal of Electronic Imaging Volume 26, Issue 5
Show Author Affiliations
Daoxiang Zhou, Key Lab. of Dependable Service Computing in Cyber Physical Society Ministry of Education (China)
Chongqing Univ. (China)
Dan Yang, Chongqing Univ. (China)
Xiaohong Zhang, Key Lab. of Dependable Service Computing in Cyber Physical Society Ministry of Education (China)
Chongqing Univ. (China)
Sheng Huang, Chongqing Univ. (China)
Shu Feng, Shanxi Agricultural Univ. (China)

© SPIE. Terms of Use
Back to Top