Title
A novel classification method for palmprint recognition based on reconstruction error and normalized distance
Abstract
In this paper, we propose a fusion classification method based on reconstruction error and normalized distance for palmprint recognition. This method first obtains an approximate representation of the test sample by solving a linear system in which the test sample is assumed to be a linear combination of all the original training samples. Then it replaces the test sample by its approximate representation and decomposes the approximate representation as a weighted sum of all the training samples. The proposed method calculates the reconstruction error of the approximate representation from the weighted sum of the training samples from each class. The method also computes the normalized distance between the test sample and each class. Finally, the method integrates the reconstruction error and normalized distance between the test sample and a class to form the matching score and assigns the test sample into the class that has the smallest matching score. Experimental results on the palmprint databases demonstrate the effectiveness of our method.
Year
DOI
Venue
2013
10.1007/s10489-012-0414-4
Appl. Intell.
Keywords
Field
DocType
Fusion classification,Approximate representation,Matching score,Palmprint recognition
Linear combination,Normalization (statistics),Linear system,Pattern recognition,Computer science,Reconstruction error,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
39
2
0924-669X
Citations 
PageRank 
References 
15
0.54
37
Authors
4
Name
Order
Citations
PageRank
Zhonghua Liu111511.12
Jiexin Pu29219.85
Tao Huang3150.88
Yong Qiu4241.66