Assessing the performance of Random Partitioning and K-Fold Cross Validation methods of evaluation of a Face Recognition System
AbstractFace recognition has been an active research area in the pattern recognition and computer vision domains due to its many potential applications in surveillance, credit cards, passport and security. However, the problem of correct method of partitioning the face data into train and test set has always been a challenge to the development of a robust face recognition system. The performance of the System was tested on locally acquired face database when the face database was randomly partitioned and when k-fold Cross Validation partition was used. The face database was captured under the condition of significant variations of rotation, illumination and facial expression. Quantitative evaluation experimental results showed that Random Sampling technique has a higher average recognition rate (96.7%) than Cross Validation partition method (95.3%). However, recognition time in Cross Validation is faster (0.36 secs) than that of Random Sampling (0.38 secs).
(1) Omidiora E. O,2006: A Prototype of Knowledge-Based System for Black Face Recognition using Principal Component Analysis and Fisher Discriminant Algorithms. Unpublished Ph. D Thesis, Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
(2) Dorigo, M. Stützle, T. (2002): The ant colony optimization metaheuristic: Algorithms applications and advances. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics. International series in Operations Research and Management Science. Kluwer Academic Publishers. Vol. 57. pp. 251-285.
(3) Dorigo, Marco., Blum, Christian. (2005): Ant colony optimization theory. A survey. Theoretical Computer Science Vol. 344 pp. 243 – 278. www.elsevier.com/locate/tcs
(4) Babatunde R. S, Olabiyisi S.O, Omidiora E.O, Ganiyu R. A. (2014): Feature Dimensionality Reduction using a Dual Level Metaheuristic Algorithm International Journal of Applied Information Systems. Vol. 7(1). pp. 49-52.
(5) Ojala T., Pietikäinen M., and Mäenpää. T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. (2002): IEEE Transactions on Pattern Analysis and Machine intelligence, Vol 24. pp. 971–987
(6) Thakur, S. Sing J. K., Basu D. K., Nasipuri, M., Kundu M. (2010): Face Recognition using Principal Component Analysis and RBF Neural Networks. IJSSST, Vol. 10(5).
(7) Omidiora E.O., Fakolujo O.A., Ayeni R.O., Olabiyisi S.O., and Arulogun O.T. (2008): Quantitative Evaluation of Principal Component Analysis and Fisher Discriminant Analysis Techniques in Face images. Journal of Computer and its Applications. Vol.15 (1). pp. 22-37.
(8) Rose R. Reena and Suruliandi A. (2011): Improving Performance of Texture Based Face Recognition Systems by Segmenting Face Region. International Journal of Network Security, Vol. 02, No. 03.Pp 23-27.
(9) Babatunde R. S, Olabiyisi S. O, Omidiora E. O, Ganiyu R. A. (2015): Local Binary Pattern And Ant Colony Optimization Based Feature Dimensionality Reduction Technique For Face Recognition System. British Journal of Computer Science and Mathematics(Article in Press)
(10) Kashef S., Nezamabadi-pour H. (2014). An advanced ACO algorithm for feature subset selection. Neurocomputing. pp. 1-9. http://dx.doi.org/10.1016/j.neucom.2014.06.067i
(11) Kavita, Chawla H.S. and Saini J.S. (2011): Parametric comparison of Ant colony optimization for edge detection problem. International Journal of Computational Engineering & Management, 13:54-58
(12) SodhiKuldeep Singh and Lal Madan. (2013): Comparative Analysis of PCA-based Face Recognition System using different Distance Classifiers. International Journal of Application on Innovation in Engineering and Management. Vol 2(7):341-348.
Authors wishing to include figures, tables, or text passages that have already been published elsewhere are required to obtain permission from the copyright owner(s) for both the print and online format and to include evidence that such permission has been granted when submitting their papers. Any material received without such evidence will be assumed to originate from the authors.
All authors of manuscripts accepted for publication in the journal Transactions on Networks and Communications are required to license the Scholar Publishing to publish the manuscript. Each author should sign one of the following forms, as appropriate:
License to publish; to be used by most authors. This grants the publisher a license of copyright. Download forms (MS Word formats) - (doc)
Publication agreement — Crown copyright; to be used by authors who are public servants in a Commonwealth country, such as Canada, U.K., Australia. Download forms (Adobe or MS Word formats) - (doc)
License to publish — U.S. official; to be used by authors who are officials of the U.S. government. Download forms (Adobe or MS Word formats) – (doc)
The preferred method to submit a completed, signed copyright form is to upload it within the task assigned to you in the Manuscript submission system, after the submission of your manuscript. Alternatively, you can submit it by email firstname.lastname@example.org