Eigenface vs Random Forest: Um Estudo Comparativo em Reconhecimento Facial
Resumo
Reconhecimento facial é uma técnica de biometria que apresenta algumas vantagens quando comparada com outras técnicas de biometria existentes. Esta técnica possui vários métodos específicos usados para reconhecimento que foram desenvolvidos em anos de pesquisa, tal como o Eigenface. Por outro lado, técnicas de Inteligência Artificial vêm sendo utilizadas para reconhecimento facial e apresentam bons resultados. Este artigo apresenta uma comparação de performance entre essas duas abordagens, aplicadas em duas base de dados de dois conhecidos benchmarks. As técnicas selecionadas foram Eigenface e Random Forest, sendo comparado os valores de taxa de erro, tempo de treinamento, tempo de classificação e memória consumida no treinamento.Referências
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Bhattacharyya, D., Ranjan, R., Alisherov, F., Choi, M., et al. (2009). Biometric authentication: A review. International Journal of u-and e-Service, Science and Technology, 2(3):13–28.
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Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Cai, D., He, X., Han, J., and Zhang, H.-J. (2006). Orthogonal laplacianfaces for face recognition. IEEE transactions on image processing, 15(11):3608–3614.
Cambridge, A. L. (2002). The database of faces. Acesso em: 02 mar. 2017.
Delac, K., Grgic, M., and Grgic, S. (2005). Independent comparative study of pca, ica, and lda on the feret data set. International Journal of Imaging Systems and Technology, 15(5):252–260.
Diniz, F. A., Neto, F. M. M., Júnior, F. d. C. L., and Fontes, L. M. O. (2013). Redface: um sistema de reconhecimento facial baseado em técnicas de análise de componentes principais e autofaces. Revista Brasileira de Computação Aplicada, 5(1):42–54.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. The annals of Statistics, pages 1–26.
El Aroussi, M. (2009). Information fusion towards a robust face recognition system.
Georgescu, D. (2011). A real-time face recognition system using eigenfaces. Journal of Mobile, Embedded and Distributed Systems, 3(4):193–204.
Gonçalves, W. N., de Andrade Silva, J., and Bruno, O. M. (2010). A rotation invariant face recognition method based on complex network. In Iberoamerican Congress on Pattern Recognition, pages 426–433. Springer.
Gupta, S., Sahoo, O., Goel, A., and Gupta, R. (2010). A new optimized approach to face recognition using eigenfaces. Global Journal of Computer Science and Technology, 10(1).
He, X., Cai, D., Yan, S., and Zhang, H.-J. (2005a). Neighborhood preserving embedding. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 2, pages 1208–1213. IEEE.
He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H.-J. (2005b). Face recognition using laplacianfaces. IEEE transactions on pattern analysis and machine intelligence, 27(3):328–340.
Heseltine, T., Pears, N., Austin, J., and Chen, Z. (2003). Face recognition: A comparison of appearance-based approaches. In Proc. VIIth Digital image computing: Techniques and applications, volume 1.
Heseltine, T. D. (2005). Face recognition: Two-dimensional and three-dimensional techniques. PhD thesis, University of York.
Jain, A. K. and Li, S. Z. (2011). Handbook of face recognition. Springer.
Jain, A. K. and Nandakumar, K. (2012). Biometric authentication: System security and user privacy. IEEE Computer, 45(11):87–92.
Khan, R., Hanbury, A., and Stoettinger, J. (2010). Skin detection: A random forest approach. In Image Processing (ICIP), 2010 17th IEEE International Conference on, pages 4613–4616. IEEE.
Kouzani, A., Nahavandi, S., and Khoshmanesh, K. (2007). Face classification by a random forest. In TENCON 2007-2007 IEEE Region 10 Conference, pages 1–4. IEEE.
Kremic, E. and Subasi, A. (2016). Performance of random forest and svm in face recognition. Int. Arab J. Inf. Technol., 13(2):287–293.
Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R news, 2(3):18–22.
Montgomery, D. C. and Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
Reddy, N. V., Krishna, D. A., Reddy, P. S., and Shirisha, R. (2011). Neural network based intelligent local fac e recognition using local pattern averaging. In 2011 3rd International Conference on Electronics Computer Technology, pages 363–367.
Tolba, A., El-Baz, A., and El-Harby, A. (2008). Face recognition: A literature review.
Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pages 586–591. IEEE.
Unar, J., Seng, W. C., and Abbasi, A. (2014). A review of biometric technology along with trends and prospects. Pattern recognition, 47(8):2673–2688.
Wayman, J., Jain, A., Maltoni, D., and Maio, D. (2005). An introduction to biometric authentication systems. Springer.
Yale, U. (1997). The yale face database. Acesso em: 02 jun. 2017.
Yang, M.-H. (2002). Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods. In Fgr, volume 2, page 215.
Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4):399–458.
Bhattacharyya, D., Ranjan, R., Alisherov, F., Choi, M., et al. (2009). Biometric authentication: A review. International Journal of u-and e-Service, Science and Technology, 2(3):13–28.
Breiman, L. (1996). Bagging predictors. Machine learning, 24(2):123–140.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Cai, D., He, X., Han, J., and Zhang, H.-J. (2006). Orthogonal laplacianfaces for face recognition. IEEE transactions on image processing, 15(11):3608–3614.
Cambridge, A. L. (2002). The database of faces. Acesso em: 02 mar. 2017.
Delac, K., Grgic, M., and Grgic, S. (2005). Independent comparative study of pca, ica, and lda on the feret data set. International Journal of Imaging Systems and Technology, 15(5):252–260.
Diniz, F. A., Neto, F. M. M., Júnior, F. d. C. L., and Fontes, L. M. O. (2013). Redface: um sistema de reconhecimento facial baseado em técnicas de análise de componentes principais e autofaces. Revista Brasileira de Computação Aplicada, 5(1):42–54.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. The annals of Statistics, pages 1–26.
El Aroussi, M. (2009). Information fusion towards a robust face recognition system.
Georgescu, D. (2011). A real-time face recognition system using eigenfaces. Journal of Mobile, Embedded and Distributed Systems, 3(4):193–204.
Gonçalves, W. N., de Andrade Silva, J., and Bruno, O. M. (2010). A rotation invariant face recognition method based on complex network. In Iberoamerican Congress on Pattern Recognition, pages 426–433. Springer.
Gupta, S., Sahoo, O., Goel, A., and Gupta, R. (2010). A new optimized approach to face recognition using eigenfaces. Global Journal of Computer Science and Technology, 10(1).
He, X., Cai, D., Yan, S., and Zhang, H.-J. (2005a). Neighborhood preserving embedding. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 2, pages 1208–1213. IEEE.
He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H.-J. (2005b). Face recognition using laplacianfaces. IEEE transactions on pattern analysis and machine intelligence, 27(3):328–340.
Heseltine, T., Pears, N., Austin, J., and Chen, Z. (2003). Face recognition: A comparison of appearance-based approaches. In Proc. VIIth Digital image computing: Techniques and applications, volume 1.
Heseltine, T. D. (2005). Face recognition: Two-dimensional and three-dimensional techniques. PhD thesis, University of York.
Jain, A. K. and Li, S. Z. (2011). Handbook of face recognition. Springer.
Jain, A. K. and Nandakumar, K. (2012). Biometric authentication: System security and user privacy. IEEE Computer, 45(11):87–92.
Khan, R., Hanbury, A., and Stoettinger, J. (2010). Skin detection: A random forest approach. In Image Processing (ICIP), 2010 17th IEEE International Conference on, pages 4613–4616. IEEE.
Kouzani, A., Nahavandi, S., and Khoshmanesh, K. (2007). Face classification by a random forest. In TENCON 2007-2007 IEEE Region 10 Conference, pages 1–4. IEEE.
Kremic, E. and Subasi, A. (2016). Performance of random forest and svm in face recognition. Int. Arab J. Inf. Technol., 13(2):287–293.
Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R news, 2(3):18–22.
Montgomery, D. C. and Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
Reddy, N. V., Krishna, D. A., Reddy, P. S., and Shirisha, R. (2011). Neural network based intelligent local fac e recognition using local pattern averaging. In 2011 3rd International Conference on Electronics Computer Technology, pages 363–367.
Tolba, A., El-Baz, A., and El-Harby, A. (2008). Face recognition: A literature review.
Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pages 586–591. IEEE.
Unar, J., Seng, W. C., and Abbasi, A. (2014). A review of biometric technology along with trends and prospects. Pattern recognition, 47(8):2673–2688.
Wayman, J., Jain, A., Maltoni, D., and Maio, D. (2005). An introduction to biometric authentication systems. Springer.
Yale, U. (1997). The yale face database. Acesso em: 02 jun. 2017.
Yang, M.-H. (2002). Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods. In Fgr, volume 2, page 215.
Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4):399–458.
Publicado
06/11/2017
Como Citar
LIMA, Jardel Ribeiro de; OLIVEIRA NETO, Rosalvo Fereira.
Eigenface vs Random Forest: Um Estudo Comparativo em Reconhecimento Facial. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 17. , 2017, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
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p. 428-441.
DOI: https://doi.org/10.5753/sbseg.2017.19517.
