A novel feature fusion approach based on blocking and its application in image recognition

Authors:
Xing Yan;Lei Cao;De-Shuang Huang;Kang Li;George Irwin
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei, Anhui, China;Artillery Academy of People Liberation Army, HeFei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei, Anhui, China;School of Electrical & Electronic Engineering, Queen’s University, Belfast;School of Electrical & Electronic Engineering, Queen’s University, Belfast
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new method of feature fusion and its application in image recognition

Pattern Recognition

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Abstract

According to the idea of canonical correlation analysis, a block-based method for feature extraction is proposed. The main process can be explained as follows: extract two groups of feature vectors from different blocks which belong to the same pattern; and then extract their canonical correlation features to form more effective discriminant vectors for recognition. To test this new approach, the experiment is performed on ORL face database and it shows that the recognition rate is higher than that of algorithm adopting single feature.