A comparative study involving classifiers and dimensionality reduction techniques applied to facial recognition
Resumo
Neste trabalho é apresentado um estudo comparativo entre as técnicas Eigenfaces e Fisherfaces combinadas com os classificadores KNN, SVM e MLP. As técnicas Eigenfaces e Fisherfaces foram utilizadas para projeção das imagens dos bancos de imagens AT&T (The database of faces) e Extended Yale B em um novo espaço de forma a se obter uma redução da dimensionalidade desses dados. Os classificadores mencionados utilizaram os dados projetados para executar a tarefa de treinamento e posterior identificação das classes dos dados de teste. Os resultados foram bastante promissores em ambos os casos, porém a rede neural MLP com a técnica Fisherfaces obtiveram os melhores resultados.
Palavras-chave:
Reconhecimento Facial, Eigenfaces, Fisherfaces, KNN, SVM, MLP
Referências
AT&T Laboratories Cambridge (2002). The database of faces.
Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7):711–720.
Coppin, B. (2010). Inteligencia Artificial. LTC.
Eleyan, A. and Demirel, H. (2007). Pca and lda based neural networks for human face recognition. In Face Recognition. InTech.
Georghiades, A. S., Belhumeur, P. N., and Kriegman, D. J. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE transactions on pattern analysis and machine intelligence, 23(6):643–660.
Giron-Sierra, J. (2016). Digital Signal Processing with Matlab Examples, Volume 2: Decomposition, Recovery, Data-Based Actions. Signals and Communication Technology. Springer Singapore.
Lee, K., Ho, J., and Kriegman, D. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence, 27(5):684–698.
Li, X., Ma, S., and Irwin, G. W. (2010). Life System Modeling and Intelligent Computing. Springer.
Martı́nez, A. M. and Kak, A. C. (2001). Pca versus lda. IEEE transactions on pattern analysis and machine intelligence, 23(2):228–233.
Müller, A. and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media.
Pedregosa, F. et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Silva, A. L. and Cintra, M. E. (2015). Reconhecimento de padrões faciais: Um estudo. In Encontro Nacional de Inteligência Artificial e Computacional, 2015, Proceedings ENIAC, pages 224–231.
Silva, G. N. d. (2009). Estudo da técnica pca (análise de componentes principais) e aufaces aplicadas ao reconhecimento de faces humanas.
Swets, D. L. and Weng, J. J. (1996). Using discriminant eigenfeatures for image retrieval. IEEE Transactions on pattern analysis and machine intelligence, 18(8):831–836.
Tan, K.-H., Kriegman, D. J., and Ahuja, N. (2002). Appearance-based eye gaze estimation. In Applications of Computer Vision, 2002.(WACV 2002). Proceedings. Sixth IEEE Workshop on, pages 191–195. IEEE.
Toygar, Ö. and Adnan, A. (2003). Face recognition using pca, lda and ica approaches on colored images. IU-Journal of Electrical & Electronics Engineering, 3(1):735–743.
Turk, M. and Pentland, A. (1991a). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1):71–86.
Turk, M. A. and Pentland, A. P. (1991b). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pages 586–591. IEEE.
Zhang, X. and Gao, Y. (2009). Face recognition across pose: A review. Pattern Recognition, 42(11):2876–2896.
Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7):711–720.
Coppin, B. (2010). Inteligencia Artificial. LTC.
Eleyan, A. and Demirel, H. (2007). Pca and lda based neural networks for human face recognition. In Face Recognition. InTech.
Georghiades, A. S., Belhumeur, P. N., and Kriegman, D. J. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE transactions on pattern analysis and machine intelligence, 23(6):643–660.
Giron-Sierra, J. (2016). Digital Signal Processing with Matlab Examples, Volume 2: Decomposition, Recovery, Data-Based Actions. Signals and Communication Technology. Springer Singapore.
Lee, K., Ho, J., and Kriegman, D. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence, 27(5):684–698.
Li, X., Ma, S., and Irwin, G. W. (2010). Life System Modeling and Intelligent Computing. Springer.
Martı́nez, A. M. and Kak, A. C. (2001). Pca versus lda. IEEE transactions on pattern analysis and machine intelligence, 23(2):228–233.
Müller, A. and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media.
Pedregosa, F. et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Silva, A. L. and Cintra, M. E. (2015). Reconhecimento de padrões faciais: Um estudo. In Encontro Nacional de Inteligência Artificial e Computacional, 2015, Proceedings ENIAC, pages 224–231.
Silva, G. N. d. (2009). Estudo da técnica pca (análise de componentes principais) e aufaces aplicadas ao reconhecimento de faces humanas.
Swets, D. L. and Weng, J. J. (1996). Using discriminant eigenfeatures for image retrieval. IEEE Transactions on pattern analysis and machine intelligence, 18(8):831–836.
Tan, K.-H., Kriegman, D. J., and Ahuja, N. (2002). Appearance-based eye gaze estimation. In Applications of Computer Vision, 2002.(WACV 2002). Proceedings. Sixth IEEE Workshop on, pages 191–195. IEEE.
Toygar, Ö. and Adnan, A. (2003). Face recognition using pca, lda and ica approaches on colored images. IU-Journal of Electrical & Electronics Engineering, 3(1):735–743.
Turk, M. and Pentland, A. (1991a). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1):71–86.
Turk, M. A. and Pentland, A. P. (1991b). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pages 586–591. IEEE.
Zhang, X. and Gao, Y. (2009). Face recognition across pose: A review. Pattern Recognition, 42(11):2876–2896.
Publicado
15/10/2019
Como Citar
ARAÚJO, Matheus; COSTA, Leonardo; SANTOS, Antony; MENEZES, Caio; CAMPOS, Gustavo.
A comparative study involving classifiers and dimensionality reduction techniques applied to facial recognition. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 832-843.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9338.
