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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.14 n.4 México Apr./Jun. 2011

 

Artículos

 

Class–Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition

 

Análisis de componentes principales probabilístico condicionado a la clase: aplicación al reconocimiento de género

 

Juan Bekios Calfa1, José M. Buenaposada2 and Luis Baumela3

 

1 Dept. de Ing. de Sistemas y Computación, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta, Chile. juan.bekios@ucn.cl.

2 Dept. de Ciencias de la Computación, Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933, Móstoles, Spain. josemiguel.buenaposada@urjc.es.

3 Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus Montegancedo s/n, 28660 Boadilla del Monte, Spain. lbaumela@fi.upm.es, http://www.dia.fi.upm.es/~pcr

 

Article received on January 29, 2010
Accepted on June 17, 2010

 

Abstract

This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naíve Bayes classifier. First it is introduced the Class–Conditional Probabilistic Principal Component Analysis (CC–PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CC–PPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement.

Keywords: Gender classification, face analysis, class conditional PPCA.

 

Resumen

Este trabajo presenta una solución al problema del reconocimiento del género de un rostro humano a partir de una imagen. Adoptamos una aproximación que utiliza la cara completa a través de la textura de la cara normalizada y redimensionada como entrada a un clasificador Náive Bayes. Presentamos la técnica de Análisis de Componentes Principales Probabilístico Condicionado–a–la–Clase (CC–PPCA) para reducir la dimensionalidad de los vectores de características para la clasificación y asegurar la asunción de independencia para el clasificador. Esta nueva aproximación tiene la deseable propiedad de presentar un modelo paramétrico sencillo para las marginales. Además, este modelo puede estimarse con muy pocos datos. En los experimentos que hemos desarrollados mostramos que CC–PPCA obtiene un 90% de acierto en la clasificación, resultado muy similar al mejor presentado en la literatura. El modelo propuesto es muy sencillo de entrenar e implementar.

Palabras clave: Clasificación de género, análisis de caras, PPCA condicionado a la clase.

 

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Acknowledgments

José M. Buenaposada and Luis Baumela gratefully acknowledge funding from the Spanish Ministerio de Educación y Ciencia under contract TIN2008–06815–C02–02. Juan Bekios–Calfa and Luis Baumela were also funded by Spanish research programme Consolider–Ingenio 2010: MIPRCV CSD2007–00018.

 

Referencias

1 Andreu, Y. , & Mollineda, R. A. (2008). The role of face parts in gender recognition. 5th International Conference on Image Analysis and Recognition. Lecture Notes in Computing Science, 5112, 945–954.         [ Links ]

2 Baluja, S., & Rowley, H. A. (2007). Boosting sex identification performance, International Journal of Computer Vision, 71 (1), 111–119.         [ Links ]

3 Bressan, M., Guillamet, D. & Vitrià, J. (2001). Using an ICA representation of high dimensional data for object recognition and classification. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001) Kauai. Hawaii, 1004–1009.         [ Links ]

4 Bressan, M., & Vitrià, J. (2002). Improving naive bayes using class–conditional ICA. Advances in Artificial Intelligence — IBERAMIA 2002, Lecture Notes in Computer Science, 2527, 1 –10.         [ Links ]

5 Cheng, J., & Greiner, R. (1999). Comparing bayesian network classifiers, Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI–99), Stockholm, Sweden, 101–108.         [ Links ]

6 Fan, L., & L. Poh, Kim, (2007). A comparative study of PCA, ICA and class–conditional ICA. 9th International Work–Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 4507, 16–22.         [ Links ]

7 Golomb, B. A., Lawrence, D. T. & Sejnowski, T. J. (1990). Sexnet: A neural network identifies sex from human faces. NIPS–31990 conference on Advances in neural information processing systems 3 (NIPS–3), Denver, CO, USA, 572–577.         [ Links ]

8 http://www.cs.waikato.ac.nz/ml/weka/, Waikato environment for knowledge analysis, weka software.         [ Links ]

9 Lapedriza, J. V. A., Marin–Jiménez, M. J. & Vitria, J. (2006). Gender recognition in non controlled environments. 18h International Conference on Pattern Recognition (ICPR 2006), Hong Kong, China, 834–837.         [ Links ]

10 Mäkinen, E., & Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (3), 541 – 547.         [ Links ]

11 Mäkinen, R. R. Erno , (2008). An experimental comparison of gender classification methods, Pattern Recognition Letters, 29 (10), 1544–1556,         [ Links ]

12 Minear, M., & Park, D. C., (2004). A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments and Computers, 36 (4), 630–633.         [ Links ]

13 Moghaddam, B., & Yang, M.–H. (2002). Learning gender with support faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (5), 707–711.         [ Links ]

14 Phillips, P., Moon, H., Rauss, P. & Rizvi, S. (2000). The feret evaluation methodology for face recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (10), 1090–1104.         [ Links ]

15 Prasad, M. N., Sowmya, A. & Koch, I. (2004). Feature subset selection using ICA for classifying emphysema in HRCT images, 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge UK, 4, 515–518.         [ Links ]

16 Shakhnarovich, G., Viola, P. A. & Moghaddam, B. (2002). A unified learning framework for real time face detection and classification, Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, D.C., 14–21.         [ Links ]

17 Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysers, Journal of the Royal Statistical Society, Series B, 61 (3), 611 –622.         [ Links ]

18 Verschae, R., Ruiz–del–Solar, J. & Corea, M. (2006) Gender classification of faces using adaboost, 11th Iberoamerican Congress on Pattern Recognition, Lecture Notes in Computer Science, 4225, 68–78.         [ Links ]

19 Viola, P., & Jones, M. J. (2004). Robust real–time face detection, International Journal of Computer Vision, 57 (2), 137–154.         [ Links ]

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