Supervised Facial Recognition Based on Multi-Resolution Analysis and Feature Alignment

被引:0
作者
Aldhahab, Ahmed [1 ]
Atia, George [1 ]
Mikhael, Wasfy B. [1 ]
机构
[1] Univ Cent Florida, Dept Elect Engn & Comp Sci, Orlando, FL 32816 USA
来源
2014 IEEE 57TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS) | 2014年
关键词
IMAGE; MULTIWAVELETS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A new supervised algorithm for face recognition based on the integration of Two-Dimensional Discrete Multiwavelet Transform (2-D DMWT), 2-D Radon Transform, and 2-D Discrete Wavelet Transform (2-D DWT) is proposed1. In the feature extraction step, Multiwavelet filter banks are used to extract useful information from the face images. The extracted information is then aligned using the Radon Transform, and localized into a single band using 2-D DWT for efficient sparse data representation. This information is fed into a Neural Network based classifier for training and testing. The proposed method is tested on three different databases, namely, ORL, YALE and subset fc of FERET, which comprise different poses and lighting conditions. It is shown that this approach can significantly improve the classification performance and the storage requirements of the overall recognition system.
引用
收藏
页码:137 / 140
页数:4
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