摘要
针对当前手背静脉识别产品较为缺乏的问题,为了促进深度学习技术在手背静脉识别领域的落地应用,提出一种基于改进多任务学习的手背静脉识别方法,同时开发一套基于软硬件协同的手背静脉识别系统。来自采集手背静脉数据集上的实验结果显示,改进算法在嵌入式设备上能够取得99.59%的准确率、0.437%的等误率、小于1 s的识别时间,足以满足大多数常见应用场景对识别性能的需求,为手背静脉识别方法的落地应用提供了一种有效的解决方案。
In order to promote the application of deep learning technology in the field of dorsal hand vein recognition,this paper proposes a dorsal hand vein recognition method based on improved multi-task learning,and develops a dorsal hand vein recognition system based on hardware and software cooperation.The experimental results on the self-collected dataset of dorsal hand vein show that the improved algorithm can achieve 99.59%accuracy,0.437%equal error rate and less than 1s recognition time on embedded devices,which is enough to meet the requirements of recognition performance in most application scenarios.The system developed above provides an effective solution for the application of the recognition method of dorsal hand vein.
出处
《信息技术与标准化》
2024年第7期20-26,共7页
Information Technology & Standardization
基金
国家重点研发计划“全颅脑声学超材料与传感阵列成像系统研制”项目,项目编号:2022YFB3204300
浙江省重点研发计划“高强度超声换能器研制及高强度声场检测关键技术研究”项目,项目编号:2022C01002。
