Optimal representation set construction with a competitive scheme for palmprint and face recognition

Abstract

Conventional representation-based methods only consider the whole training set for representation and recognition. However, some of the training samples make little contribution to the representation of the test sample. In this paper, we propose to construct an optimal representation set for recognition. First, the nearest neighbour principle is used to initialize the optimal representation set. Then, it will be updated through adding a training sample which can work with the current representation set to represent the test sample with minimum error. With this scheme, we accelerate the computation procedure by the partitioned matrix technique. To fully validate the effectiveness of the proposed method, experiments have been conducted on public palmprint and face databases by comparing the proposed method with the state-of-the-art methods. The proposed competitive sample selection method shows promising results.

Keywords:

• palmprint recognition
• face recognition
• sample selection
• linear regression
• partitioned matrix

2010 AMS Subject Classification:

• 15A06

Acknowledgements

This work was supported by the National Nature Science Committee of China under Grant Nos. 61202276.