Conference Proceeding

Palmprint Recognition with Multiple Correlation Filters Using Edge Detection for Class-Specific Segmentation

Carnegie Mellon Univ., Pittsburgh;
07/2007; DOI:10.1109/AUTOID.2007.380622 ISBN: 1-4244-1300-1 pp.214 - 219 In proceeding of: Automatic Identification Advanced Technologies, 2007 IEEE Workshop on
Source: IEEE Xplore

ABSTRACT We present a new series of results that show the competitive performance of advanced correlation filter classifiers for palmprint recognition. We design multiple correlation filters in subregions of the palmprint for each class. We propose a segmentation stage that selects palmprint subregions to train the filters in a class-by-class basis using different edge-detection operators. This effectively guides the filter training process to rely on regions that have a stronger line content, increasing between-class separation of the palm-prints. We evaluate the proposed algorithm in a large palmprint database of 385 classes. Our preliminary results show that most classes can be perfectly separated and the average equal error rates are as low as 0.0003% for regions of interest of size 64 times 64 pixels.

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Keywords

average equal error rates
 
between-class separation
 
class-by-class basis
 
different edge-detection operators
 
filter training process
 
guides
 
large palmprint database
 
new series
 
palmprint
 
palmprint recognition
 
segmentation stage
 
selects palmprint subregions
 
size 64 times 64 pixels
 
stronger line content