Face Recognition Based Attendance System
Mekala V1, Vibin Mammen Vinod2, Manimegalai M3, Nandhini K4
1Mekala V, Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
2Vibin Mammen Vinod, Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
3Manimegalai M, Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 26 September, 2019. | Manuscript published on October 10, 2019. | PP: 520-525 | Volume-8 Issue-12, October 2019. | Retrieval Number: L34061081219/2019©BEIESP | DOI: 10.35940/ijitee.L3406.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The objective of the attendance system is to provide an alternative means to the traditional attendance system which consumes 10 to 15 minutes of time in 50 minutes of lecture hour. It also aims at eliminating human errors and proxy in recording the attendance of the student. This can be achieved by using face recognition for monitoring the attendance of the students in a class. The face recognition process is carried out by using the Cognitive Face API which follows the Principal Component Analysis (PCA) algorithm. Initially, the dataset of the students in a class are collected. The dataset is collected in a manner that for each student, a set of 25 images in various angles is collected. The features are extracted from the images that are collected by using the cognitive face API and the database is formed. The image of the class in columns is acquired immediately, when the input image is acquired by using a mechanical set up which captures image based on hour, the number of faces in the input image is detected. The detected faces are cropped and then stored in a folder. The features of the cropped faces are also extracted and it is compared and matched with the features in the database. When the feature matches, the attendance is marked for the particular student in the spreadsheet and then the attendance report of the class is being uploaded in the web-page. Thus, the attendance of the student can be recorded in an effective manner. This paper also helps in avoiding human error which is unavoidable.
Keywords: Cognitive Face API, Principal Component Analysis, IoT, Arduino UNO,HTML.
Scope of the Article: IoT