Precise eye localization using HOG descriptors

In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes. Finally, we compare the eye detection results with three state-of-the-art works and a commercial software. The results show that our algorithm achieves the highest accuracy on the FERET and FRGCv1 databases, which is the most complete comparative presented so far. © Springer-Verlag 2010.This work has been partially supported by the grant TEC2009-09146 of the Spanish Government.Monzó Ferrer, D.; Albiol Colomer, A.; Sastre, J.; Albiol Colomer, AJ. (2011). Precise eye localization using HOG descriptors. Machine Vision and Applications. 22(3):471-480. https://doi.org/10.1007/s00138-010-0273-0S471480223Riopka, T., Boult, T.: The eyes have it. In: Proceedings of ACM SIGMM Multimedia Biometrics Methods and Applications Workshop, Berkeley, CA, pp. 9–16 (2003)Kim C., Choi C.: Image covariance-based subspace method for face recognition. Pattern Recognit. 40(5), 1592–1604 (2007)Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 3, San Diego, CA, pp. 164–171 (2005)Amir A., Zimet L., Sangiovanni-Vincentelli A., Kao S.: An embedded system for an eye-detection sensor. Comput. Vis. Image Underst. 98(1), 104–123 (2005)Zhu Z., Ji Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Comput. Vis. Image Underst. 98(1), 124–154 (2005)Huang, W., Mariani, R.: Face detection and precise eyes location. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, Washington, DC, USA, pp. 722–727 (2000)Brunelli R., Poggio T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)Guan, Y.: Robust eye detection from facial image based on multi-cue facial information. In: Proceedings of IEEE International Conference on Control and Automation, pp. 1775–1778 (2007)Rizon, M., Kawaguchi, T.: Automatic eye detection using intensity and edge information. In: Proceedings of TENCON, vol. 2, Kuala Lumpur, Malaysia, pp. 415–420 (2000)Han, C., Liao, H., Yu, K., Chen, L.: Fast face detection via morphology-based pre-processing. In: Proceedings of the 9th International Conference on Image Analysis and Processing, vol. 2. Springer, London, UK, pp. 469–476 (1997)Song J., Chi Z., Liu J.: A robust eye detection method using combined binary edge and intensity information. Pattern Recognit. 39(6), 1110–1125 (2006)Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye localization through a general-to-specific model definition. In: Proceedings of the British Machine Vision Conference, Edinburgh, Scotland, pp. 187–196 (2006)Smeraldi F., Carmona O., Bign J.: Saccadic search with gabor features applied to eye detection and real-time head tracking. Image Vis. Comput. 18(4), 323–329 (1998)Sirohey S. A., Rosenfeld A.: Eye detection in a face image using linear and nonlinear filters. Pattern Recognit. 34(7), 1367–1391 (2001)Ma, Y., Ding, X., Wang, Z., Wang, N.: Robust precise eye location under probabilistic framework. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 339–344 (2004)Lu, H., Zhang, W., Yang D.: Eye detection based on rectangle features and pixel-pattern-based texture features. In: Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems, pp. 746–749 (2007)Jin, L., Yuan, X., Satoh, S., Li, J., Xia, L.: A hybrid classifier for precise and robust eye detection. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, Hong Kong, pp. 731–735 (2006)Vapnik V. N.: The Nature of Statistical Learning Theory. Springer, New York Inc, New York, NY (1995)Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 1, Hawaii, pp. 511–518 (2001)Fasel I., Fortenberry B., Movellan J.: A generative framework for real time object detection and classification. Comput. Vis. Image Underst. 98(1), 182–210 (2005)Huang J., Wechsler H.: Visual routines for eye location using learning and evolution. IEEE Trans. Evolut. Comput. 4(1), 73–82 (2000)Behnke S.: Face localization and tracking in the neural abstraction pyramid. Neural Comput. Appl. 14(2), 97–103 (2005)Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 9th European Conference on Computer Vision, vol. 2, San Diego, CA, pp. 886–893 (2005)Albiol A., Monzo D., Martin A., Sastre J., Albiol A.: Face recognition using hog-ebgm. Pattern Recognit. Lett. 29(10), 1537–1543 (2008)Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)Bicego, M., Lagorio, A., Grosso, E., Tistarelli M.: On the use of SIFT features for face authentication. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition Workshop, New York, p. 35 (2006)Yang M.-H., Kriegman D., Ahuja N.: Detecting faces in images: a survey. Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)Jain A., Murty M., Flynn P.: Data clustering: a review. ACM Comput. Syst. 31(3), 264–323 (1999)Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)Humanscan, BioID database. http://www.bioid.comPeer, P.: CVL Face database, University of Ljubjana. http://www.fri.uni-lj.si/enPhillips P. J., Moon H., Rizvi S. A., Rauss P. J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Jin, C., Hoffman, K., Marques, J., Jaesik, M., Worek, W.: Overview of the face recognition grand challenge. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 1, San Diego, CA, pp. 947–954 (2005)Jesorsky, O., Kirchberg, K.J., Frischholz, R.: Robust face detection using the hausdorff distance. In: Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication, Springer, London, UK, pp. 90–95 (2001)Neurotechnologija, Biometrical and Artificial Intelligence Technologies, Verilook SDK. http://www.neurotechnologija.comWitten I., Frank E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn: Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)Turk M., Pentland A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991

Machine Analysis of Facial Expressions

No abstract

Contributions on Automatic Recognition of Faces using Local Texture Features

Uno de los temas más destacados del área de visión artifical se deriva del análisis facial automático. En particular, la detección precisa de caras humanas y el análisis biométrico de las mismas son problemas que han generado especial interés debido a la gran cantidad de aplicaciones que actualmente hacen uso de estos mecnismos. En esta Tesis Doctoral se analizan por separado los problemas relacionados con detección precisa de caras basada en la localización de los ojos y el reconomcimiento facial a partir de la extracción de características locales de textura. Los algoritmos desarrollados abordan el problema de la extracción de la identidad a partir de una imagen de cara ( en vista frontal o semi-frontal), para escenarios parcialmente controlados. El objetivo es desarrollar algoritmos robustos y que puedan incorpararse fácilmente a aplicaciones reales, tales como seguridad avanzada en banca o la definición de estrategias comerciales aplicadas al sector de retail. Respecto a la extracción de texturas locales, se ha realizado un análisis exhaustivo de los descriptores más extendidos; se ha puesto especial énfasis en el estudio de los Histogramas de Grandientes Orientados (HOG features). En representaciones normalizadas de la cara, estos descriptores ofrecen información discriminativa de los elementos faciales (ojos, boca, etc.), siendo robustas a variaciones en la iluminación y pequeños desplazamientos. Se han elegido diferentes algoritmos de clasificación para realizar la detección y el reconocimiento de caras, todos basados en una estrategia de sistemas supervisados. En particular, para la localización de ojos se ha utilizado clasificadores boosting y Máquinas de Soporte Vectorial (SVM) sobre descriptores HOG. En el caso de reconocimiento de caras, se ha desarrollado un nuevo algoritmo, HOG-EBGM (HOG sobre Elastic Bunch Graph Matching). Dada la imagen de una cara, el esquema seguido por este algoritmo se puede resumir en pocos pasos: en una primera etapa se extMonzó Ferrer, D. (2012). Contributions on Automatic Recognition of Faces using Local Texture Features [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16698Palanci

Facial Point Detection using Boosted Regression and Graph Models

Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors