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http://dx.doi.org/10.7236/IJASC.2020.9.4.192

A Study on Face Recognition and Reliability Improvement Using Classification Analysis Technique  

Kim, Seung-Jae (Department of Convergence Honam University)
Publication Information
International journal of advanced smart convergence / v.9, no.4, 2020 , pp. 192-197 More about this Journal
Abstract
In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.
Keywords
Machine Learning; SVM; PCA; Classification; Face Recognition;
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Times Cited By KSCI : 7  (Citation Analysis)
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1 Tom M. Mitchell, "The discipline of machine learning(Vol. 9)," Carnegie Mellon University, Shcool of Computer Science, MachineLearning Department, 2006. DOI:https://www.researchgate.net/publication/268201693_The_Discipline_of_Machine_Learning.
2 I. P. Alonso, D. F. Llorca, M. A. Sotelo, and L. M. Bergasa, "Combination of Feature Extraction Methods for SVM Pedestrian Detection," IEEE Trans. on TITS, Vol. 8, No. 2, pp. 292-307, June 2007. DOI: 10.1109/TITS.2007.894194   DOI
3 Seong-Jun Kim, "A Wavelet-based Profile Classification using Support Vector Machine," Vol.18, NO.5, ppl718-723, 2008. DOI:https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE01086741.   DOI
4 Chang, Jae-Young, "Automatic Retrieval of SNS Opinion Document Using Machine Learning Technique," The Journal of The Institute of Internet, Broadcasting and Communication, VOl. 13, No. 5, October 2013. DOI:http://dx.doi.org/10.7236/JIIBC.2013.13.5.27.   DOI
5 Min, Meekyung, "Classification of Seoul Metro Stations Based on Boarding/Alighting Patterns Using Machine Learning Clustering," The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 18, No. 4, pp.13-18, Aug. 31, 2018. pISSN 2289-0238, eISSN 2289-0246 DOI:https://doi.org/10.7236/JIIBC.2018.18.4.13.   DOI
6 Young Jin Kim, Joung Woo Ryu, Won Moon Song and Myung Won Kim, "Fire Probability Prediction Based on Weather Information Using Decision Tree," Journal of KIISE, JOK:software and application", Vol.40, No.11, 2013.11. DOI:http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE02283313
7 Um, Nam-Kyoung , Woo, Sung-Hee and Lee, Sang-Ho, "The Hybrid Model using SVM and Decision Tree for Intrusion Detection," KIPS Transactions on Computer and Communication Systems, Vol.14, No.1, pp.1-6, 2007. DOI: 10.3745/KIPSTC.2007.14.1.1
8 Jonghoo Choi and Doosung Seo, "Decision Trees and Its Applications," Statistics Korea: The study of Statistics Analysis, Vol.4, NO.1, pp.61-83, 1999. DOI: http://kostat.go.kr/attach/journal/4-1-3.PDF
9 Jun Heon Lee and Jun Geol Baek, "Real-time control chart using a random forest-based multi-category classifier," Korean Institute Of Industrial Engineers, pp. 673-682, 2017.11 DOI:http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07262460
10 Kim, Pan Jun, "An Analytical Study on Automatic Classification of Domestic Journal articles Using Random Forest," Journal of the Korean Society for information Management , vol.36. no.2, pp.57-77, 2018. DOI: https://doi.org/10.3743/KOSIM.2018.35.2.037.   DOI
11 Yun, Tae-Gyun and Yi, Gwan-Su, "Application of Random Forest algorithm for the decision support system of medical diagnosis with the selection of significant clinical test," The Transaction of the Korean Institute of Electrical Engineers, 57(6), pp.1058-1062, 2008. DOI:https://www.koreascience.or.kr/article/JAKO200822179196823.pdf
12 S.E. El-Khamy, O.A. Abdel-Alim and M.M. Saii, "Neural Network Face Recognition Using Statistical Feature Extraction," Radio Science Conference, 2000. 17th NRSC '2000. Seventeenth National, pp. C31/1-C31/8, 2000. DOI: 10.1109/NRSC.2000.838960   DOI
13 Yang, Jae-Wan, Lee, Young-Doo and Koo, In-Soo, "Sensor Fault Detection Scheme based on Deep Learning and Support Vector Machine," The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 18, No. 2, pp.185-195, Apr. 30, 2018. pISSN 2289-0238, eISSN 2289-0246 DOI:http://doi.org/10.7236/JIIBC.2018,18.2.185.   DOI
14 Md. Omar Faruqe and Md. Al Mehedi Hasan, "Face Recognition Using PCA and SVM,", Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on, pp. 97-101, 2009. DOI: 10.1109/ICASID.2009.5276938   DOI