Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.

中文翻译：

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基于深度学习的手势非接触式身份认证系统的设计与实现

基于手势的手势语数字具有多种非接触式应用。应用程序包括针对残障人士（例如老年人和残疾人）的通信，医疗保健应用程序，汽车用户界面以及安全和监视。这项工作提出了一个完整的基于端到端深度学习的边缘计算系统的设计和实现，该系统可以使用“身份验证代码”以非接触方式验证用户。“认证码”是一个“ n”位数字代码，并且这些数字是手语数字的手势。我们提出了一种记忆有效的深度学习模型来对手语数字的手势进行分类。所提出的深度学习模型基于瓶颈模块，该模块受深度残差网络的启发。该模型的分类精度为99。公开提供的手语数字集的1％。该模型部署在Raspberry pi 4 Model B边缘计算系统上，用作用户验证的边缘设备。边缘计算系统包括两个步骤，它首先从与其相连的摄像机实时获取输入并将其存储在缓冲区中。在第二步中，模型通过将缓冲区中的第一张图像作为输入，以280毫秒的推理速率对数字进行分类。