Using Classification (Machine Learning) for Automatic Attendance Management
Introduction to Problem
Every institution, be it a School, College, Organisation, or Workplace, all use one common function, which is attendance. Attendance management is essential to every institution, as it not only helps in checking the performance of organisation’s workers, staff members, and students but also helps in keeping them in their right place of work. As long as institutions have been there, the traditional way of keeping attendance records have been by using an attendance sheet and pen. This system is repetitive, and also tedious and time taking. If the number of students or staff members is relatively high, then there are chances of proxy attendance which is hard to control, and the time loss too will be higher. For this, an automatic attendance registering system based upon facial recognition can prove to be a good solution.
Many organisations around the world have started using biometric systems in place of this tedious process of attendance, and though all biometric traits are not that efficient, the face is. This is the reason why Facial Recognition is a good alternative to attendance management. They are being used in security purposes and can be efficiently used for attendance registering. Face recognition measures facial structures, including nose, mouth and distance between eyes. For this, the system will be first fed with images of the face as input data set which is then used as a source, and the scanner scans faces, matches it with existing data set, and if a match is found, it records the person's attendance. 
Importance of the Problem
The problem of attendance recording through traditional methods has some backdrops. They are:
Due to these problems, an automated attendance registering system which uses facial recognition can easily replace traditional method.
Benefits of Machine learning (Classification):
Machine learning is the key to implement such a solution to an age-old tedious method of attendance registering. The solution being developed can use k-NN classification and deep metric learning for highly accurate and real-time detection and recording. 
The system will operate through three functions, which are:
As the automated system uses facial recognition for attendance registering, there can be three broad types of data which should be the input. These data sets become input at two stages. The first stage is Machine Learning stage, and the second stage is Real-time implementation.
The data collection of the third stage input can be done by cameras, which can be two in number. Here the cameras can act as a scanner. As soon as the live stream is retrieved, the video frame is captured and is converted into grayscale. This grayscale feed is then fed into a feature extractor which starts detecting facial features, essentially faces. The faces are then aligned as per system and encoded. This process creates input data. The facial data thus collected is then sent to a matcher. 
Class Label Outputs
The input data goes into the Matching module. Here, each encoded faces constitutes the input data set. Here comes the use of classification. Each of these input data sets acts as a discrete value, and a matching process starts. This process starts matching the discrete input value with the much larger primary set database of students and then each face from the database is matched with the input set to predicts which student is it, or is it not. 
If the Detection ID is confirmed, at first camera, which is at the ENTRY side of the classroom, in inward direction then the output of the system comes as YES. At output YES, the ENTRY is recorded in a report. If the ID isn't confirmed, the output is NO, and the frame is skipped, and the next frame is brought on to record more.
If the Detection ID is confirmed at the second camera, which is at the EXIT side of the classroom in an outward direction, the output of the system comes as YES, and the EXIT is recorded. If the ID is not confirmed, then the output of the system becomes NO. Here, the action which follows this output is skipping frame, and the process continues.
Since, both the entry and exit of each of the student is being recorded, the following decisions can be taken:
Input Features Example
The input features include -
These above input features combined together will form the complete input facial feature set.
The output classes and values will be:
The classroom used here as an example of working of this solution has 100 students. First, images and facial features of each of these 100 students is made the primary data set. Since the attendance recording will be carried out through two different cameras, the number of samples of input can vary from 1 face to up to 10 faces all in the range of the camera. Every time, the camera will tend to record faces, and total sample size at one instance of classroom entry or exit should be 100 for a class of 100 students. For this, the students will have to face the camera for entering and exiting the classroom. The number of instances too will vary from 6 on both of the cameras (owing to 6 different classes) to more of students keep moving out or in as per will. For a class of 100 students, these instances can even go up to 1000 times or even more.
Similar research works
Many similar attendance registering systems are either in the research phase or are already being used all over the world. Some of the prominent ones are:
Though there are various works being carried out in using machine learning based face recognition systems, all are security-based and hence use a much complex facial recognition algorithms. These use colour images for better detection. But, when we use it for a school attendance registering system, it won't be fruitful. If the same process of used as discussed above, the cost will be drastically low and the process will be much more effective than that of Fingerprint recognition systems.
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