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Intelligent Data Mining For Automatic Face Recognition

Year 2013, Volume: 3 Issue: 2, 97 - 101, 23.07.2016

Abstract

The advancement in computer science and information technology is one of the most important characteristics of the century. One of the important consequences of this advancement is the availability of huge number of automated databases which are waiting to be exploited. This exploitation will lead to knowledge discovery which will help the decision making processes in many fields. In this paper a knowledge discovery, data mining, artificial intelligent technique called Logical Analysis of Data (LAD) is introduced and applied to the well know problem of face recognition. Knowledge discovered in the form of patterns is saved and then used in a machine learning system in order to identify the already learned faces, and to distinguish them from unknown faces. The results show that LAD is promising approach to pattern recognition

References

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  • Bishop, C.M. (2006). Pattern recognition and machine learning (Vol. 4): springer New York. Bores, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation of logical analysis of data. Knowledge and Data Engineering, IEEE Transactions on, 12(2), 292-306.
  • Bouckaert, R.R., Frank, E., Hall, M.A., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2010).
  • WEKA---Experiences with a Java Open-Source Project. The Journal of Machine Learning Research, 11, 2533-2541.
  • Bozdogan, H. (2003). Statistical data mining and knowledge discovery: Chapman & Hall/CRC. Crama, Y., Hammer, P.L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research, 16(1), 299-325.
  • Dupuis, C., Gamache, M., & Pagé, J.F. (2012). Logical analysis of data for estimating passenger show rates at Air Canada. Journal of Air Transport Management, 18(1), 78-81.
  • Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12): Springer. Hammer, P.L., & Bonates, T.O. (2006). Logical analysis of data—an overview: from combinatorial optimization to medical applications. Annals of Operations Research, 148(1), 203-225.
  • Hammer, P.L., Kogan, A., & Lejeune, M.A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications.
  • Hammer, P.L., Kogan, A., Simeone, B., & Szedmák, S. (2004). Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics, 144(1), 79-102.
  • Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J., & Budynek, J. (1998). The Japanese female facial expression (JAFFE) database.
  • Mortada, M.A., Carroll, T., Yacout, S., & Lakis, A. (2009). Rogue components: their effect and control using logical analysis of data. Journal of Intelligent Manufacturing, 1-14.
  • Mortada, M.A., Yacout, S., & Lakis, A. (2010). Fault Diagnosis of Power Transformers Using Logical Analysis of Data. APPLICABILITY AND INTERPRETABILITY OF LOGICAL ANALYSIS OF DATA IN CONDITION BASED MAINTENANCE, 74.
  • Mortada, M.A., Yacout, S., & Lakis, A. (2011). Diagnosis of rotor bearings using logical analysis of data. Journal of Quality in Maintenance Engineering, 17(4), 371-397.
  • Ryoo, H.S., & Jang, I.Y. (2009). Milp approach to pattern generation in logical analysis of data. Discrete Applied Mathematics, 157(4), 749-761.
  • Salamanca, D. (2008). The logical analysis of data applied to conditionbased maintenance. Msc thesis, École Polytechnique, Montréal, Canada.
  • Witten, I.H., Frank, E., & Hall, M.A. (2011). Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann.
Year 2013, Volume: 3 Issue: 2, 97 - 101, 23.07.2016

Abstract

References

  • Belhumeur, P.N., Hespanha, J.P., & Kriegman, D.J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7), 711-720.
  • Bishop, C.M. (2006). Pattern recognition and machine learning (Vol. 4): springer New York. Bores, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000). An implementation of logical analysis of data. Knowledge and Data Engineering, IEEE Transactions on, 12(2), 292-306.
  • Bouckaert, R.R., Frank, E., Hall, M.A., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2010).
  • WEKA---Experiences with a Java Open-Source Project. The Journal of Machine Learning Research, 11, 2533-2541.
  • Bozdogan, H. (2003). Statistical data mining and knowledge discovery: Chapman & Hall/CRC. Crama, Y., Hammer, P.L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research, 16(1), 299-325.
  • Dupuis, C., Gamache, M., & Pagé, J.F. (2012). Logical analysis of data for estimating passenger show rates at Air Canada. Journal of Air Transport Management, 18(1), 78-81.
  • Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12): Springer. Hammer, P.L., & Bonates, T.O. (2006). Logical analysis of data—an overview: from combinatorial optimization to medical applications. Annals of Operations Research, 148(1), 203-225.
  • Hammer, P.L., Kogan, A., & Lejeune, M.A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications.
  • Hammer, P.L., Kogan, A., Simeone, B., & Szedmák, S. (2004). Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics, 144(1), 79-102.
  • Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J., & Budynek, J. (1998). The Japanese female facial expression (JAFFE) database.
  • Mortada, M.A., Carroll, T., Yacout, S., & Lakis, A. (2009). Rogue components: their effect and control using logical analysis of data. Journal of Intelligent Manufacturing, 1-14.
  • Mortada, M.A., Yacout, S., & Lakis, A. (2010). Fault Diagnosis of Power Transformers Using Logical Analysis of Data. APPLICABILITY AND INTERPRETABILITY OF LOGICAL ANALYSIS OF DATA IN CONDITION BASED MAINTENANCE, 74.
  • Mortada, M.A., Yacout, S., & Lakis, A. (2011). Diagnosis of rotor bearings using logical analysis of data. Journal of Quality in Maintenance Engineering, 17(4), 371-397.
  • Ryoo, H.S., & Jang, I.Y. (2009). Milp approach to pattern generation in logical analysis of data. Discrete Applied Mathematics, 157(4), 749-761.
  • Salamanca, D. (2008). The logical analysis of data applied to conditionbased maintenance. Msc thesis, École Polytechnique, Montréal, Canada.
  • Witten, I.H., Frank, E., & Hall, M.A. (2011). Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann.
There are 16 citations in total.

Details

Other ID JA56NC94YB
Journal Section Articles
Authors

Ahmed Ragab This is me

Soumaya Yacout This is me

Mohamed-salah Ouali This is me

Publication Date July 23, 2016
Published in Issue Year 2013 Volume: 3 Issue: 2

Cite

APA Ragab, A., Yacout, S., & Ouali, M.-s. (2016). Intelligent Data Mining For Automatic Face Recognition. TOJSAT, 3(2), 97-101.
AMA Ragab A, Yacout S, Ouali Ms. Intelligent Data Mining For Automatic Face Recognition. TOJSAT. July 2016;3(2):97-101.
Chicago Ragab, Ahmed, Soumaya Yacout, and Mohamed-salah Ouali. “Intelligent Data Mining For Automatic Face Recognition”. TOJSAT 3, no. 2 (July 2016): 97-101.
EndNote Ragab A, Yacout S, Ouali M-s (July 1, 2016) Intelligent Data Mining For Automatic Face Recognition. TOJSAT 3 2 97–101.
IEEE A. Ragab, S. Yacout, and M.-s. Ouali, “Intelligent Data Mining For Automatic Face Recognition”, TOJSAT, vol. 3, no. 2, pp. 97–101, 2016.
ISNAD Ragab, Ahmed et al. “Intelligent Data Mining For Automatic Face Recognition”. TOJSAT 3/2 (July 2016), 97-101.
JAMA Ragab A, Yacout S, Ouali M-s. Intelligent Data Mining For Automatic Face Recognition. TOJSAT. 2016;3:97–101.
MLA Ragab, Ahmed et al. “Intelligent Data Mining For Automatic Face Recognition”. TOJSAT, vol. 3, no. 2, 2016, pp. 97-101.
Vancouver Ragab A, Yacout S, Ouali M-s. Intelligent Data Mining For Automatic Face Recognition. TOJSAT. 2016;3(2):97-101.