Abstract
Face detection is a mature problem in computer vision. While diverse high performing face detectors have been proposed in the past, we present two surprising new top performance results. First, we show that a properly trained vanilla DPM reaches top performance, improving over commercial and research systems. Second, we show that a detector based on rigid templates - similar in structure to the Viola&Jones detector - can reach similar top performance on this task. Importantly, we discuss issues with existing evaluation benchmark and propose an improved procedure.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Al-Shehri, S.A.: A simple and novel method for skin detection and face locating and tracking. In: Masoodian, M., Jones, S., Rogers, B. (eds.) APCHI 2004. LNCS, vol. 3101, pp. 1–8. Springer, Heidelberg (2004)
Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: CVPR (2012)
Benenson, R., Mathias, M., Tuytelaars, T., Van Gool, L.: Seeking the strongest rigid detector. In: CVPR (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Divvala, S.K., Efros, A.A., Hebert, M.: How important are deformable parts in the deformable parts model? In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part III. LNCS, vol. 7585, pp. 31–40. Springer, Heidelberg (2012)
Dollár, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 645–659. Springer, Heidelberg (2012)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC (2009)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (2008)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI (2010)
Garcia, C., Delakis, M.: Convolutional face finder: A neural architecture for fast and robust face detection. PAMI (2004)
Hoiem, D., Chodpathumwan, Y., Dai, Q.: Diagnosing error in object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 340–353. Springer, Heidelberg (2012)
Jain, V., Learned-Miller, E.: Fddb: A benchmark for face detection in unconstrained settings. Tech. Rep. UM-CS-2010-009, University of Massachusetts, Amherst (2010)
Jain, V., Learned-Miller, E.: Online domain adaptation of a pre-trained cascade of classifiers. In: CVPR (2011)
Kalal, Z., Matas, J., Mikolajczyk, K.: Weighted sampling for large-scale boosting. In: Everingham, M., Needham, C.J., Fraile, R. (eds.) BMVC. British Machine Vision Association (2008)
Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In: ICCV BeFIT Workshop (2011)
Li, J., Zhang, Y.: Learning surf cascade for fast and accurate object detection. In: CVPR (2013)
Ma, K., Ben-Arie, J.: Vector array based multi-view face detection with compound exemplars. In: CVPR (2012)
Maji, S., Berg, A., Malik, J.: Classi cation using intersection kernel support vector machines is efficient. In: CVPR (2008)
Mathias, M., Timofte, R., Benenson, R., Van Gool, L.: Traffic sign recognition - how far are we from the solution? In: ICJNN (2013)
Osadchy, M., LeCun, Y., Miller, M.: Synergistic face detection and pose estimation with energy-based models. JMLR (2007)
Rizzi, A., Gatta, C., Marini, D.: A new algorithm for unsupervised global and local color correction. Pattern Recognition Letters (2003)
Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI (1998)
Ruiz-del-Solar, J., Verschae, R.: Skin detection using neighborhood information. In: FG, Seoul, Korea, May 17-19, pp. 463–468 (2004)
Schapire, R.E.: Explaining adaboost. In: Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik (2013)
Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. IJCV (2004)
Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: CVPR (2013)
Shen, X., Lin, Z., Brandt, J., Wuk, Y.: Detecting and aligning faces by image retrieval. In: CVPR (2013)
SkyBiometry, http://www.skybiometry.com
Trzcinski, T., Christoudias, C.M., Fua, P., Lepetit, V.: Boosting binary keypoint descriptors. In: CVPR (2013)
Viola, P., Jones, M.: Robust real-time face detection. IJCV (2004)
Walk, S., Majer, N., Schindler, K., Schiele, B.: New features and insights for pedestrian detection. In: CVPR (2010)
Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: CVPR (June 2014)
Yan, J., Zhang, X., Lei, Z., Li, S.: Face detection by structural models. Image and Vision Computing (2013)
Yan, J., Zhang, X., Lei, Z., Li, S.: Real-time high performance deformable model for face detection in the wild. In: ICB (2013)
Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Tech. rep., Microsoft Research (2010)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L. (2014). Face Detection without Bells and Whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-10593-2_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10592-5
Online ISBN: 978-3-319-10593-2
eBook Packages: Computer ScienceComputer Science (R0)