I-Corps: Automated Attendance Check by Using Smartphone Cameras

在教室等情况下，检查出勤情况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，或者让学生在考勤表上签名。然而，这种传统的方法面临着两个问题：当学生人数很多的时候，读学生的名字可能会占用几分钟的讲课时间，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上签名，也可以在缺席的同学的名字上签名；对于教师来说，通过手动查看每个学生的考勤表来计算一个学期每个学生的总出勤率是不可取的任务。这个I-Corps团队提出了一种高效而准确的方法来完成这项任务。通过使用智能手机摄像头拍摄教室中学生的面部视频，该团队提出了一个统一的视觉人脸检测、跟踪和识别算法框架，可以同时识别视频中的多张人脸。建议的系统有以下步骤：教师在自己的智能手机上安装建议的应用程序；在第一堂课上，教师使用智能手机摄像头拍摄学生在教室里的短时间视频。应用程序将自动为课程建立一个人脸数据集，教师只需要在第一堂课上识别他们；在剩下的课程中，教师会为每节课录制视频，应用程序会自动检查出勤情况。所提出的智能手机应用程序将执行多目标跟踪，将检测到的人脸（包括假阳性）关联到人脸轨迹（每个轨迹包含具有姿势，光照等变化的同一个体的多个实例）中，然后将每个人脸轨迹中的人脸实例聚类成少量簇，实现冗余较少的稀疏人脸表示。

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.