Open Access Open Access  Restricted Access Subscription or Fee Access

Facial Recognition Based Attendance System

Shubham Rewari, Apurv Shaha, Sankaradithyan Gunasekharan

Abstract


In today’s fast paced world, for an organization with around 500 employees, a manual attendance system is highly inefficient and time consuming. Traditional methods of automatic attendance systems like fingerprint, RFID or iris scans are easy to bypass, as the biometric features, such systems take into consideration are far less than facial features. Our facial recognition system is used to detect a person’s face and then compare it with the stored facial database to recognize it. Once the face is recognized, his attendance is marked, along with his in-time and out-time, and stored in a database. This paper proposes a system which uses Haar cascade method for facial detection integrated with principal component analysis (PCA) technology for facial recognition. This whole process is carried out on a raspberry Pi B+ module using OpenCV (Open Source Computer Vision) library installed on it. The attendance database is created in MySQL which keeps a record of employee’s in-time and out-time. Proposed biometric face recognition system is basically used in three domains: employee management, leave management, time attendance system and last but not the least, it can be used as authorization and access control system. 

Cite this Article:

Shubham Rewari, Apurv Shaha, Sankaradithyan Gunasekharan. Facial Recognition Based Attendance System. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(2):     43–49p.


Keywords


Raspberrypi, OpenCV, Viola Jones algorithm, PCA, face detection and recognition

Full Text:

PDF

References


Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, et al. Study of Implementing Automated Attendance System Using Face

Recognition Technique. International Journal of Computer and Communication Engineering (IJCCE). Jul 2012; 1(2).

Ajinkya Patil, Mrudang Shukla. Implementation of Classroom Attendance System Based on Face Recognition in Class. International Journal of Advances in Engineering & Technology (IJAET). Jul 2014.

Ekenel HK, Stallkamp J, Gao H, et al. Face Recognition for Smart Interactions, interACT Research. Computer Science Department, University at Karlsruhe (TH).

Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, et al. Study of Implementing Automated Attendance System Using Face Recognition Technique. International Journal of Computer and Communication Engineering (IJCCE). Jul 2012; 1(2).

Kyungnam Kim. Face Recognition using Principle Component Analysis. Department of Computer Science, University of Maryland, College Park, MD 20742, USA.

Kandla Arora. Real Time Application of Face Recognition Concept. International Journal of Soft Computing and Engineering (IJSCE). Nov 2012; 2(5). ISSN: 2231-2307.

Goldstein AJ, Harmon LD, Lesk AB. Identification of Human Faces. In Proc. IEEE Conference on Computer Vision and Pattern Recognition. May 1971; 59: 748– 760p.

Herbert Bay, Andreas Ess, Tinne Tuytelaars, et al. Surf: Speeded up Robust Features. Computer Vision and Image Understanding (CVIU). 2008; 110(3): 346–359p.

Javier Ruiz Del Solar, Rodrigo Verschae, Mauricio Correa. Face Recognition in Unconstrained Environments: A Comparative Study. In ECCV Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition, Marseille, France. Oct 2008.

Kyungnam Kim. Face Recognition using Principle Component Analysis. Department of Computer Science, University of Maryland, College Park, MD 20742, USA.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/