期刊
SENSORS
卷 21, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/s21144896
关键词
biometrics; palmprint recognition; triple-type feature descriptors; matching score fusion
资金
- Key Disciplines of Guizhou Province-Computer Science and Technology [ZDXK[2018]007]
- Science and Technology Foundation of Guizhou Province [QianKeHeJiChu-ZK[2021]YiBan 334]
- Research Projects of Innovation Group of Guizhou Provincial Department of Education [QianJiaoHeKY[2021]022]
- Guizhou Provincial Service Industry Development Guide fund project [QianFaGaiFuWu[2018]1181]
This paper proposes a heuristic palmprint recognition method by extracting triple types of features without requiring any training samples. Experimental results show the promising effectiveness of the proposed method on three widely used palmprint databases.
Palmprint recognition has received tremendous research interests due to its outstanding user-friendliness such as non-invasive and good hygiene properties. Most recent palmprint recognition studies such as deep-learning methods usually learn discriminative features from palmprint images, which usually require a large number of labeled samples to achieve a reasonable good recognition performance. However, palmprint images are usually limited because it is relative difficult to collect enough palmprint samples, making most existing deep-learning-based methods ineffective. In this paper, we propose a heuristic palmprint recognition method by extracting triple types of palmprint features without requiring any training samples. We first extract the most important inherent features of a palmprint, including the texture, gradient and direction features, and encode them into triple-type feature codes. Then, we use the block-wise histograms of the triple-type feature codes to form the triple feature descriptors for palmprint representation. Finally, we employ a weighted matching-score level fusion to calculate the similarity between two compared palmprint images of triple-type feature descriptors for palmprint recognition. Extensive experimental results on the three widely used palmprint databases clearly show the promising effectiveness of the proposed method.
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