Deep Multi-loss Hashing Network for Palmprint Retrieval and Recognition

Wei Jia, Shuwei Huang, Bin Wang, Lunke Fei, Yang Zhao, Hai Min
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引用次数: 7

Abstract

With the wide application of biometrics technology, the scale of biometrics databases is increasing rapidly. In this situation, fast retrieval technology is more and more necessary for large-scale biometrics retrieval and recognition. Palmprint recognition is one of the emerging biometrics technologies. However, the research on fast palmprint retrieval algorithm is still preliminary. Hashing is one of the most popular image retrieval technologies due to its fast speed and low storage cost. In this paper, we propose a new deep palmprint hashing method, which integrates classification loss, pairing loss and quantization loss in a unified deep learning framework. Experimental results show that the proposed deep multi-loss hashing method has better performance for palmprint recognition and retrieval than other existing classic hashing methods.
基于深度多损失哈希网络的掌纹检索与识别
随着生物特征识别技术的广泛应用,生物特征数据库的规模也在迅速增长。在这种情况下,快速检索技术对大规模生物特征检索和识别越来越有必要。掌纹识别是新兴的生物识别技术之一。然而,快速掌纹检索算法的研究还处于初级阶段。哈希算法具有速度快、存储成本低的优点,是目前最流行的图像检索技术之一。本文提出了一种新的深度掌纹哈希方法,该方法将分类损失、配对损失和量化损失集成在一个统一的深度学习框架中。实验结果表明,所提出的深度多重损失哈希方法比现有的经典哈希方法具有更好的掌纹识别和检索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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