I-Corps: Automated Attendance Check by Using Smartphone Cameras

在教室等场景中检查出勤率通常需要教师通过阅读花名册上的名字来逐个识别每个学生，或者要求学生在出勤表上签名。然而，这种传统的方法面临两个问题：当学生人数较多时，阅读学生的名字可能会占用几分钟的讲课时间，并且让学生在考勤表上签名容易被欺骗，因为他们可以在自己的名字和缺席的同学的名字上签名;教师若要计算每名学生在一个学期内的总出勤率，并不理想。这个I-Corps团队提出了一个有效和准确的方法来完成这项任务。通过使用智能手机摄像头在教室中拍摄学生面部视频，该团队提出了一个统一的视觉面部检测，跟踪和识别算法框架，以同时识别视频中的多张人脸。拟议的系统有以下步骤：教师在自己的智能手机上安装拟议的应用程序;在第一堂课上，教师使用智能手机摄像头在教室里拍摄学生面部的短时间视频。该应用程序将自动为课程构建人脸数据集，教师只需在第一堂课上识别它们;在其余课程中，教师将拍摄每堂课的视频，应用程序将自动进行考勤。申报的智能手机应用程序将执行多对象跟踪，以将检测到的面部（包括误报）关联到面部轨迹片段（每个轨迹片段包含同一个人的多个实例，姿态、照明等各不相同）。然后将每个人脸轨迹片段中的人脸实例聚类为少量的簇，从而实现具有较少冗余的稀疏人脸表示。

Checking attendance in scenarios such as classrooms commonly needs an instructor to recognize each student one by one by reading the names on a roster or ask students to sign up the attendance sheet. However, this traditional method faces two problems: reading students' names may occupy minutes of lecture time when the number of students is large and letting students to sign up an attendance sheet is prone to be cheated since they can sign their own names and their classmates' names who are absent in the class; it is not a desirable task for instructors to calculate the total attendance of every student in a semester by going through every attendance sheet manually. This I-Corps team proposes an efficient and accurate way to accomplish this task. By taking videos of student faces in classrooms using Smartphone cameras, the team proposes a unified framework of visual face detection, tracking and recognition algorithms to recognize multi-faces in the video simultaneously.The proposed system has the following steps: instructors install the proposed App on their own Smartphones; in the first class, instructors use the Smartphone cameras to take a short-period video of student faces in the classroom. The application will automatically build a face dataset for the course and the instructor only needs to identify them for the first class; in the remaining classes, instructors take videos of each class and the application will do automated attendance check. The proposed Smartphone App will perform multi-object tracking to associate detected faces (including false positives) into face tracklets (each tracklet contains multiple instances of the same individual with variations in pose, illumination etc.) and then the face instances in each face tracklet are clustered into a small number of clusters, achieving sparse face representation with less redundancy.