کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
534225 | 870236 | 2011 | 6 صفحه PDF | دانلود رایگان |
Constantly, the assumption is made that there is an independent contribution of the individual feature extraction and classifier parameters to the recognition performance. In our approach, the problems of feature extraction and classifier design are viewed together as a single matter of estimating the optimal parameters from limited data. We propose, for the problem of facial recognition, a combination between an Interest Operator based feature extraction technique and a k-NN statistical classifier having the parameters determined using a pattern search based optimization technique. This approach enables us to achieve both higher classification accuracy and faster processing time.
► Feature extraction and classifier design treated as a single problem.
► The optimization technique employed represents a variant of Pattern Search.
► Error rates on AT&T and UMIST databases: 2.9% respectively 1.9%.
Journal: Pattern Recognition Letters - Volume 32, Issue 9, 1 July 2011, Pages 1250–1255