论文标题
通过深层蒸馏散发识别有效的无约束棕榈印刷
Towards Efficient Unconstrained Palmprint Recognition via Deep Distillation Hashing
论文作者
论文摘要
深层掌上识别已成为一个新兴的问题，具有在手持和可穿戴消费设备上进行个人身份验证的巨大潜力。先前对棕榈印刷识别的研究主要基于受控环境中专用设备收集的受约束数据集，这必须降低灵活性和便利性。此外，一般的深棕榈识别算法通常太重了，无法满足嵌入式系统的实时要求。在本文中，建立了一个新的棕榈印刷基准，该基准由5个智能手机收集的20,000多个图像以不受约束的方式组成。每个图像都用14个关键点（ROI）提取的14个关键点标记。此外，提出了称为“深蒸馏片”（DDH）的方法作为有效的深棕榈印刷识别的基准。 Palmprint图像转换为二进制代码，以提高特征匹配的效率。源自知识蒸馏，构建新型蒸馏损失功能以压缩深层模型，以进一步提高光网络上特征提取的效率。全面的实验都是对受约束和无约束的棕榈印记数据库进行的。使用DDH，棕榈印刷识别的准确性可以提高高达11.37％，并且掌上验证的均等错误率（EER）可以降低高达3.11％。结果表明我们的数据库的可行性，DDH可以胜过其他基线以实现最先进的性能。收集的数据集和相关的源代码可在http://gr.xjtu.edu.edu.cn/web/bell/resource上公开获得。
Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint recognition are mainly based on constrained datasets collected by dedicated devices in controlled environments, which has to reduce the flexibility and convenience. In addition, general deep palmprint recognition algorithms are often too heavy to meet the real-time requirements of embedded system. In this paper, a new palmprint benchmark is established, which consists of more than 20,000 images collected by 5 brands of smart phones in an unconstrained manner. Each image has been manually labeled with 14 key points for region of interest (ROI) extraction. Further, the approach called Deep Distillation Hashing (DDH) is proposed as benchmark for efficient deep palmprint recognition. Palmprint images are converted to binary codes to improve the efficiency of feature matching. Derived from knowledge distillation, novel distillation loss functions are constructed to compress deep model to further improve the efficiency of feature extraction on light network. Comprehensive experiments are conducted on both constrained and unconstrained palmprint databases. Using DDH, the accuracy of palmprint identification can be increased by up to 11.37%, and the Equal Error Rate (EER) of palmprint verification can be reduced by up to 3.11%. The results indicate the feasibility of our database, and DDH can outperform other baselines to achieve the state-of-the-art performance. The collected dataset and related source codes are publicly available at http://gr.xjtu.edu.cn/web/bell/resource.
