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Biometric Attendance Prediction using Face Recognition Method

Affiliations

  • Department of IT, VIT University, Vellore – 632 014, Tamil Nadu, India

Abstract


Objectives: Face recognition has arisen as a smart solution to discourse many present-day needs for empathy and the confirmation of identity claims. It fetches together the capacity of other biometric methods, which endeavor to tie uniqueness for the individual characteristic features of the body, and the further familiar functionality of visual reconnaissance systems. Face acknowledgment is a vital field for authentication purpose particularly in the case of student's attendance. This paper is intended at applying a digitized system for attendance recording. Methods/Statistical Analysis: In this process two methods are used to determine the face recognition attendance system- Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA). PCA is a statistical procedure that uses an orthogonal modification to transform a set of observations of probably correlated variables into a set of values of linearly uncorrelated variables. LDA is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear permutation of features that shows or separates two or more classes of objects or events. Findings: However the current system of tracking attendance via records is difficult to manage. Face recognition based attendance system deals with the maintenance of the student's daily attendance details. Biometric appreciation comprises alike, within an open-mindedness of calculation, of pragmatic biometric behaviors in contradiction of formerly poised data for a focus. Estimated identical is obligatory due to the dissimilarities in biological characteristics and deeds both within and among persons. It generates the attendance for the student on basis of presence in the class. Application/Improvements: Project can be modernized in nearby future as, when a responsibility for the same arises, as it is very flexible in positions of growth. And the enrichment approach of camera formation based on the result of the position valuation in order to progress the face detection effectiveness.

Keywords

Biometric Features, Computer Vision Communities, Machine Learning, Pattern Recognition.

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References


  • Sajid M, Hussain R, Usman M. A conceptual model for automated attendance marking system using facial recognition. 2014 IEEE Ninth International Conference on Digital Information Management (ICDIM); 2014. p. 7–10. Crossref
  • Kawaguchi Y, Shoji T, Weijane LIN, Kakusho K, Minoh M. Face recognition-based lecture attendance system. The 3rd AEARU Workshop on Network Education; 2005. p. 70–5.
  • Hjelmas E, Low BK. Face detection: A survey. Computer vision and image understanding. 2001; 83(3):236–74. Crossref
  • Abdallah, Abdallah S, Lynn Abbott A, El-Nasr MA. A new face detection technique using 2D DCT and self organizing feature map. Proc of World Academy of Science Engineering and Technology. 2007; 21:15–9.
  • Ephraim T, Himmelman T. Optimizing Viola-Jones Face Detection for use in Webcams; 2014.
  • Kumar S, Bhuyan MK, Chakraborty BK. Extraction of informative regions of a face for facial expression recognition. IET Computer Vision. 2016; 10(6):567–76. Crossref
  • Tokiwa Y, Nonobe K, Iwatsuki M. Web-based tools to sustain the motivation of students in distance education. FIE’09. 39th IEEE in Frontiers in Education Conference; 2009. p. 1–5. Crossref
  • Chang CL, Li E, Wen Z. Rendering Novel Views of faces Using Disparity Estimation; 2000.
  • Turk MA, Pentland AP. Face recognition using eigen faces. CVPR’91. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 1991. p. 586–91.
  • Li X, Areibi S. A hardware/software co-design approach for face recognition. ICM’04. Proceedings of The 16th International Conference on Microelectronics; 2004. p. 55–8.
  • Shehu V, Dika A. Using real time computer vision algorithms in automatic attendance management systems. 2010 IEEE 32nd International Conference on Information Technology Interfaces (ITI); 2010. p. 397–402.
  • Pan X. Research and implementation of Access Control System based on RFID and FNN-Face Recognition. 2012 IEEE 2nd International Conference on Intelligent System Design and Engineering Application (ISDEA); 2012. p. 716–9. Crossref PMid:22244688
  • Er MJ, Wu S, Lu J, Toh HL. Face recognition with radial basis function (RBF) neural networks. IEEE Transactions on Neural Networks. 2002; 13(3):697–710. Crossref PMid:18244466
  • Girosi F, Poggio T. Networks and the best approximation property. Biological Cybernetics. 1990; 63(3):169–76. Crossref
  • Graham DB, Allinson NM. Characterising virtual eigen signatures for general purpose face recognition. In Face Recognition. Springer Berlin Heidelberg; 1998. p. 446–56. Crossref
  • Haykin S. Neural networks: a comprehensive foundation Prentice-Hall Upper Saddle River. NJ MATH Google Scholar; 1999.
  • Liu Q, Tang X, Lu H, Ma S. Kernel scatter-difference based discriminant analysis for face recognition. ICPR’04. IEEE Proceedings of the 17th International Conference on Pattern Recognition. 2004; 2:419–22.
  • Moody J, Darken CJ. Fast learning in networks of locally-tuned processing units. Neural Computation. 1989; 1(2):281–94. Crossref
  • Samal A, Iyengar PA. Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern recognition. 1992; 25(1):65–77. Crossref
  • 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; 1(2):100. Crossref
  • Nagendrarajah J, Perera MUS. Recognition of expression variant faces-a principle component analysis based approach for access control. 2010 IEEE International Conference on Information Theory and Information Security (ICITIS); 2010. p. 125–9. Crossref
  • Fernandes S, Bala J. Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition. International Journal of Signal Processing Systems. 2013; 1(1):1–6. Crossref

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