Pehchaan: A Touchless Attendance System

Prerak Moolchandani, Shreya Hegde, Muskan Hassanandani, Garv Jhangiani, G. Bhatia, A. Tewari, S. Dugad
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Abstract

With the Coronavirus pandemic taking its toll all over the world, and social distancing measures being adopted, there is an urgent need to digitise all the processes for the smooth functioning of organisations. Thus, Pehchaan presents a nocontact system for recording the attendance of entities by verifying face and voice. It makes use of low-cost ESP microcontrollers with a camera and microphone module to extract face measurements using a Deep Convolutional Neural Network and apply Mel Frequency Cepstrum techniques to the audio files. We verify the entities’ claim by comparing the similarity with the encodings stored in the database. We are using wifi networks to connect ESP with the backend server. Face and voice recognition together act as two-factor verification and an admin will be able to access the records of a particular day and time and thus would be able to capture the attendance without any manual effort.
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Pehchaan:一个非接触式考勤系统
随着冠状病毒大流行在世界各地造成损失,以及采取社交距离措施,迫切需要将所有流程数字化,以使组织顺利运作。因此,Pehchaan提出了一种非接触式系统,通过验证面部和声音来记录实体的出勤情况。它使用低成本的ESP微控制器,带有摄像头和麦克风模块,使用深度卷积神经网络提取面部测量数据,并将Mel频率倒频谱技术应用于音频文件。我们通过比较与数据库中存储的编码的相似性来验证实体的声明。我们使用wifi网络连接ESP和后端服务器。面部和语音识别一起作为双因素验证,管理员将能够访问特定日期和时间的记录,因此无需任何人工操作即可捕获出勤情况。
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