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

Occlusion Coherence: Detecting and Localizing Occluded Faces

The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances

Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

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Dense 3D Face Correspondence

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