My Ssec Capstone Project Automatic Attendance through Face Recognition M

Automatic Attendance through Face Recognition M

Automatic Attendance through Face Recognition
Jahanzeb Jamshed
Department of Computer Science
IQRA University (IU), Karachi

Abstract— In this paper, we propose a system that takes the attendance of students for classroom lecture. Our system takes the attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using each result of face recognition independently because the face detection rate is not silently high. In this paper, we propose a method for estimating the attendance precisely using all the results of face recognition obtained by continuous observation. Continuous observation improves the performance for the estimation of the attendance We constructed the lecture attendance system based on face recognition and applied the system to classroom lecture. This paper first reviews the related works in the field of attendance management and face recognition. Then, it introduces our system structure and plan. Finally, experiments are implemented to provide as evidence to support our plan. The result shows that continuous observation improved the performance for the estimation of the attendance.
Keywords—face recognition, face detection
Though the video streaming service of lecture archive is readily available in many systems, students have few opportunities to view the lecture in this service because lecture content is not summarized. If the attendance of a student of classroom lecture is attached to the video streaming service, it is possible to present the video of the time when he was absent. It is important to take the attendance of the students in the classroom automatically.
ID tag or other identi?cations such the record of log in/out in most e-Learning systems are not su?cient because it does not represent students’ context in face-to- face classroom. 1 It is also di?cult to grasp the contexts by the data of a single moment. Student’s context such as presence, seat position, status, and comprehension are discussed in this paper. At the same time face images re?ect a lot about this con-text information. It is possible to estimate automatically whether each student is present or absent and where each student is sitting by using face recognition technology. It is also possible to know whether students are awake or sleeping and whether students are interested or bored in lecture if face images are annotated with the students’ name, the time and the place.
We are concerned with the method to use face image processing technology. By continuously observing of face information, our approach can solve low e?ectiveness of existing face detection technology and improve the accuracy of face recognition.
We propose a method that takes the attendance using face recognition based on continuous observation. In this paper, our purpose is to obtain the attendance, positions and images of students’ face, which are useful information in the classroom lecture

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In this paper, our system consists of one camera. Camera will be on ceiling to obtain the seats where the students are sitting then the camera which is in front of the seats to capture images of student’s face. The procedure of our system consists of the following steps (Shown in Figure 2.1).

Experimental Setup
(Figure 2.1)

1. Seats information processing: this process determines the target seat to direct the camera. We adopt the approach called Active Student Detecting method (ASD). The idea of this approach is to estimate the existence of a student sitting on the seat by using the background subtraction and inter-frame subtraction of the image from the sensing camera on the ceiling.
2. Capturing Images: Our system selects all the faces from the estimated sitting area obtained by ASD, directs the camera to the faces and captures images.
3. The system processes the face images, the face images are detected from the captured image, archived and recognized. Face detection data and face recognition data are recorded into the database.
4. Attendance information processing: this process estimates the attendance by interpreting the face recognition data obtained by continuous observation. The module obtains the most likely correspondence between the students and the seats under the constrained condition. The system regards a student corresponded to each seat as present. The position and attendance of the student are recorded into the database 2. The procedure is repeated during lecture within 10-15 minutes and estimated the attendance of the students in real time.

b) Estimating Student’s Existence:

We use the method of ASD to estimate the existence of a student facing the camera. It is described in detail in above. In this approach, an observation CCTV camera is installed on the ceiling of the classroom and looks down at the student area vertically 3. ASD estimates students’ existence by using the background subtraction and inter-frame subtraction of the images captured by the CCTV camera. In the background subtraction method, noise factors like bags and coats of the students are also detected, and the students are not detected if the color of clothes of them are similar the seats. ASD makes use of the inter-frame subtraction to detect the moving of the students.

c) Capturing Criteria

Capturing criteria module selects the student facing the camera within the estimated sitting area in order to determine where to direct the CCTV camera. Actually, in this paper, the module selects seats by scanning the seats sequentially 4. This approach is insufficient because it wastes time directing the camera to where the student-and -seat the seats the student’s correspondence is already decided in other words, if we take multiple faces at a time within the prescribed 15minutes of continuously capturing images of all the student faces with in the defined range so that it will work accurately and mark the student’s attendance accordingly.

d) Face Detection and Recognition

Face detection and recognition module detects faces from the image captured by the camera, and the image of the face is cropped and stored. The module recognizes the images of student’s face, which have been registered manually with their names and ID codes in the database 5. Face detection data and face recognition data are recorded into the database.

We propose a system that provides a solution to the above mentioned problems by automating the process of attendance management that can be used during exams or a lecture which will save effort and time. The system consists of a camera that captures the image of the classroom and sends it to the image processing module which then forwards it to the comparison module at the beginning of the session. In the processing module the image is enhanced to facilitate the matching process. After this face detection and recognition is performed. The image is captured again at the end of the session, sent to the processing module and forwarded to the comparison module again. At this junction both the images are compared and the students who are present in both the images are marked present in the database. In case a student is present whose face is not recognized, the lecturer can update the system manually.

This section describes the software algorithm for the system.
The algorithm consists of the following steps
• Image acquisition
• Histogram normalization
• Noise removal
• Skin classification
• Face detection
• Face recognition
• Update the attendance sheet in Excel
In the first step image is captured from the camera. There are illumination effects in the captured image because of different lightning conditions and some noise which is to be removed before going to the next steps.

a) Image Acquisition

Image is acquired from the camera that is connecting above the board. A camera capture images for fifteen minutes and send the computer for processing.

b) Histogram normalization

Color images converted to grace scale image for increasing contrast.

c) Noise removal

In this system, use the medium filter for the removal of noise and other filter like FFT, low pass filter also removes the noise in the input image.

d) Skin classification

It is used for the increasing the efficiency of the face detection algorithm. It is related with binary image by using the thresholding of skin colors.

e) Face detection

At this stage faces are detected by marking the rectangle on the faces of the student. After the detection of face the next step is cropping of each detected face. The algorithm uses the technique which increase the speed of algorithm. each crop image is assign to a separate thread for the recognition purposes.

Image shows the face detection
(Figure 3.1)

f) Face Recognition

After the face detection next step is face recognition. This can be done by cropping the detected face and compare with the database. In this way face of student. Verified one by one and attendance is marked on the computer.

Image shows the face recognition
(Figure 3.2)

g) Update The Attendance Sheet In Excel

Whenever a detected face matches with a person in the database, the value is updated in that particular Excel sheet.
This is carried out through the function.
• The Candidates identify is determined through the index of the image with which the detected face matches with.
• A spread sheet of the desired format has to be drafted beforehand.
• Using the index values corresponding cell in the sheet is updated with one along with the time and date.

v. Conclusion
In this system, we have implemented an attendance system for a lecture, section or laboratory by which lecturer or teaching assistant can record student’s attendance. It saves times and effort, especially if it is a lecture with huge number of students. Automated Attendance System has been envisioned for the purpose of reducing the drawbacks in the traditional(manual) system. This attendance system demonstrates the use of image processing techniques in classroom. This system can not only merely help in the attendance system, but also improve the goodwill of an institution.

The authors would like to thank Iqra University and all my teachers especially our supervisor Prof. Engr. Dr. Muhammad Zamin Ali Khan for providing us needed guidance to complete this report.

1. M.T.a.A. Pentland, “Eigenfaces For Recognition,” Journal of Cognitive Neuroscience vol.3, no.1, pp. 586-591,1991.
2. A.V.a.R.Tokas,”Fast Face Recognition Using Eigen Faces,”IJRITCC,vol.2, no. 11,pp.3615-3618,November 2014.
3. Paul Viola and Michael J. Jones,”Robust Real-Time Face Detection,”International Journal of Computer Vision,vol. 57,no.2,pp. 137-154, May 2004.
4. N.J.M.M.K.a.H.A.Mayank Agarwal,”Face Recognition Using Eigenface approach,”IRCSE,vol.2,no.4,pp. 1793-8201, August 2010.
5. Viney Hermath, Ashwini Mayakar, “Face Recognition Using Eigen Faces and,”IACSIT,vol. 2,no.4, pp.1793-8201, August 2010.