Smart Group Attendance Monitoring System using Face Recognition

DOI : 10.17577/IJERTCONV8IS13041

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Smart Group Attendance Monitoring System using Face Recognition

  1. Kumuda S

    Dept of Electronics and Communication Engineering

    NIE Institute of Technology, Mysuru-570018, India

  2. Meghashree M N

    Dept of Electronics and Communication Engineering

    NIE Institute of Technology, Mysuru-570018, India

    [3] Harish S V Assistant professor

    Department of Electronics and Communication Engineering


    Abstract – Database containing the train test images are compared with the captured images of students in the group of four to five is resized to be compared with individual test image

    .If the attendance has to be taken then they need to login into GUI interface for start capturing the image its done that there is transparency in the attendance and there is clarity for each and every student. Including to the model, features like attendance percentage and message to the parent is sent if the student is not present for consecutive three days .A App is created for the updating of the student attendance and percentage and also the test scores is available with it. The system is more reliable than the older and existing systems and also has good time management with faster processing speed.

    KeywordsSURF Algorithm, Image acquisition.


      Recognition of face technology is growing and becoming an major aspect in the research field of the Computer vision automated system. It has been playing a major role for the analysis around the world. Face recognition is mainly used in the field of Security, Authentication, Attendance marking. marking the attendance is a hectic job for the teachers in colleges and universities. Different institutes have adopted the different methods of attendance marking. The traditional way of attendance marking are calling the name of students, passing the attendance sheet to the students. The popular attendance marking system which are used are Radio- Frequency Identification and Detection(RFID), Iris recognition and fingerprint recognition. Since Iris recognition and fingerprint recognition requires the making of queue thus it is more time taking and hetic in nature.These methods are very short distance biometrics-based attendance marking system, but in our proposed system requires the person to be at a medium distance from the camera, which is fixed in the centre of the front wall facing all the students. The range of the camera covers all the students in the classroom. It carries the face recognition task through an image/video stream for the recording of attendance of lectures and maintaining the database in excel sheets and updating it online and interface with GUI system.

      This model uses SURF descriptor for image enhancement and cropping of image to resize to individual image to store it in

      database. The Face recognition based attendance monitoring system mainly focuses on the matching of individual and unique features in the face of the individuals.

      Our proposed system take care of group attendance and accuracy of identifying the persons is tested using several test cases. The model takes into consideration of various scenarios like noise, processing of image and other various facial expression and change in human facial (absence or presence of spectacles) for processing of data.


      Individual students picture is stored in the database. Using MATLAB software GUI is created for each student. When the program is run a tick will be sounded which indicates the user to hold students photo. After a certain delay the image is captured is the test image. Again on one more tick, the user must hold the internal marks of a student and it is captured in the image format. The OCR function is called and the texts in the image are extracted. If the train and test images are matched then the corresponding students GUI is opened. For the face recognition purpose again SURF detection and description technique itself is used. Also for image to text conversion purpose Optical Character Recognition (OCR) technique is used. The internal mark is stored there in text format. On clicking the average option the average of best two marks is obtainedIndividual images of each student is captured and stored in the database called Train images. These images are to be captured with a camera having good resolution and with proper illumination. On each day the capturing of the images in the class is done. This image will be a group image of many individuals. This image is stored in another folder called Test image. Now for the marking of attendance Train images are compared with the Test images. The SURF algorithm is used for the matching purpose. SURF is a descriptor which detects the interest points in the Train images and searches for the same features in the test image. It then filters out few points and left with few interesting points. Based on the Euclidean distance, the points having minimum distances are matched.

      Fig -1: Flowchart of the experimental setup.

      Technical Requirement

      Hardware Requirements

      1.Pentium 4 and onwards. 2.RAM – 1GB and more. 3.HDD – 80GB and more. 4.Camera.

      Software requirements

      1. MATLAB

      2. Speeded Up Robust Features (SURF).


      This section describes the SURF software algorithm for the system. Speeded Up Robust Features (SURF) is a scale and in- plane rotation invariant feature. The Face recognition based attendance mainly focuses on the matching of individual and unique features in the face of the individuals. SURF has two main parts that is detector and descriptor where detector gives out the main focus points in the images and descriptor gives the information about the features of shapes and all details of the interest points. SURF algorithm uses Hessian approximation. The SURF has major 2 working points . The key point of circular where the main focus of the image like eyes are formed and other rectangular for descriptor for in formation of shapes .

      The algorithm consists of the following steps stages:

      1.Image acquisition 2.Image processing

      3.Distinctive characteristic location 4.Template creation

      5.Template matching

      In this model, the camera attached at the wall captures image of the student in group of three to four and then it resizes and cropped into individual student images to compare with the train image stored in the database.

      Image Acquisition

      The camera attached to the wall capture images of students in different angles and every 6 minutes and it is processed and resized and cropped and then sent to the GUI

      Creation of Students Database

      20-25 photographs of each student are captured in different angles with some modification and gestures. The images are cropped and converted into grey scale for the reduction in the time of computation. All the images are put into the folder named Student Database, each folder is further divided into sub-folders, subfolder is named on the name of the student. Sub-folder contains multiple images of each student.

      Face Detection and Eye Detection:

      In the wake of introducing the camera in the classroom, it catches the edges containing the characteristics of all understudies sitting in the class. SURF is connected on this edge, which identifies the appearances in the edge. To guarantee that the identified question is confront, each distinguished protest is edited and additionally handled for eye location and if eyes are recognized they are considered as faces else are rejected.

      p>Browse image

      The image is browsed from the local disk of the system. This image is a group image of all trained faces.

      GUI (Graphical User Interface)

      . It consists an area for the 2-dimensional image which is going to be either captured from the webcam or browsed from the local disk of the system as shown in fig 2.

      Fig -2: GUI of Real Time face recognition-based attendance monitoring system


      1. MATLAB: MATLAB is a high level language used in image processing and high level application for security.

      2. SURF: Speeded Up Robust Features is a algorithm which in the image processing and image comaparsion .It uses detector and descriptor


      Fig -3: System flowchart

    6. RESULT

      Fig -4: Face Detection

      Fig -5:Images stored in the database.

      Fig -6: Updation of attendance into MS EXCEL

      Fig -7: face recognition and attendance


      This model uses SURF algorithm for detection of images and then it is compared with the test image and then stored into database and the information is shared through the app for the teachers, parents and also students.

      If the ward is absent for continuous days, then an mail and message is dispatched to the parent. This system has fast image processing than existing method.


      This work was supported by our project guide, Mr Harish S V, Assistant Professor, ECE, NIEIT. We are thankful to the guide and faculties of our college who helped us in proposing this system.


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