By: Shirish Mohan Dubey, Anubhav Anand, Bhavya Agrawal, and Garvit Mathur
1- Student, Department of Computer Science Engineering, Poornima College of Engineering, Jaipur, India
2- Student, Department of Computer Science Engineering, Poornima College of Engineering, Jaipur, India
3- Student, Department of Computer Science Engineering, Poornima College of Engineering, Jaipur, India
4- Student, Department of Computer Science Engineering, Poornima College of Engineering, Jaipur, India
The goal of this project is to develop a facial recognition-based attendance system for educational institutions to improve and modernize the current system and make it more effective and efficient than it was in the past. The distinctive features of the human face allow for individual identification. Since there is little chance of a face deviating or being duplicated, it is utilized to trace identification. 
The article also covers the advantages of putting automated attendance systems in place, such as increased security, convenience, accuracy, and efficiency. It showcases case studies and real-world implementations from a range of industries, highlighting the usefulness and success stories of implementing face recognition-based attendance systems. To provide dependable and durable attendance tracking systems, it looks at how cameras, image processing techniques, machine learning models, and database management systems can be integrated. PCA, SVM, viola jones, OCR are the algorithms that are used. Once the student’s face was identified, the attendance was updated on an Excel page.
Face recognition, face detection, OCR, PCA, SVM, viola jones
Citation:
Refrences:
- Mehta P, Tomar P. An efficient attendance management system based on face recognition using MATLAB and Raspberry Pi 2. International Journal of Engineering Technology Science and Research IJETSR. 2016 May;3(5):71-8.
- Jeng SH, Liao HY, Han CC, Chern MY, Liu YT. Facial feature detection using geometrical face model: An efficient approach. Pattern recognition. 1998 Mar 1;31(3):273-82.
- Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. In Computer Vision—ECCV’96: 4th European Conference on Computer Vision Cambridge, UK, April 15–18, 1996 Proceedings, Volume I 4 1996 (pp. 43-58). Springer Berlin Heidelberg.
- Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proceedings of 12th international conference on pattern recognition 1994 Oct 9 (Vol. 1, pp. 582-585). IEEE.
- Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996 Jan 1;29(1):51-9.
- Edwards GJ, Taylor CJ, Cootes TF. Interpreting face images using active appearance models. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition 1998 Apr 14 (pp. 300-305). IEEE.
- Cootes TF, Edwards GJ, Taylor CJ. Active appearance models. IEEE Transactions on pattern analysis and machine intelligence. 2001 Jun;23(6):681-5.
- Turk M, Pentland A. Eigenfaces for recognition. Journal of cognitive neuroscience. 1991 Jan 1;3(1):71-86.
- Rath SK, Rautaray SS. A survey on face detection and recognition techniques in different application domain. International Journal of Modern Education and Computer Science. 2014 Aug 1;6(8):34.
- Swets DL, Weng JJ. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on pattern analysis and machine intelligence. 1996 Aug;18(8):831-6.
- Paul LC, Al Sumam A. Face recognition using principal component analysis method. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 2012 Nov;1(9):135-9.
- Dagher I. Incremental PCA-IDA algorithm. In2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications 2010 Sep 6 (pp. 97-101). IEEE.
- Kawaguchi Y, Shoji T, Lin W, Kakusho K, Minoh M. Face recognition-based lecture attendance system. InThe 3rd AEARU workshop on network education 2005 Oct (pp. 70-75).
- Kar N, Debbarma MK, Saha A, Pal DR. Study of implementing automated attendance system using face recognition technique. International Journal of computer and communication engineering. 2012 Jul 1;1(2):100-3.
- Ali NS, Alhilali AH, Rjeib HD, Alsharqi H, Al-Sadawi B. Automated attendance management systems: systematic literature review. International Journal of Technology Enhanced Learning. 2022;14(1):37-65.
