Face Detection Coupling Texture, Color and Depth Data
- 2.3k Downloads
In this chapter, we propose an ensemble of face detectors for maximizing the number of true positives found by the system. Unfortunately, combining different face detectors increases both the number of true positives and false positives. To overcome this difficulty, several methods for reducing false positives are tested and proposed. The different filtering steps are based on the characteristics of the depth map related to the subwindows of the whole image that contain the candidate faces. The most simple and easiest criteria to use, for instance, is to filter the candidate face region by considering its size in metric units.
The experimental section demonstrates that the proposed set of filtering steps greatly reduces the number of false positives without decreasing the detection rate. The proposed approach has been validated on a dataset of 549 images (each including both 2D and depth data) representing 614 upright frontal faces. The images were acquired both outdoors and indoors, with both first and second generation Kinect sensors. This was done in order to simulate a real application scenario. Moreover, for further validation and comparison with the state-of-the-art, our ensemble of face detectors is tested on the widely used BioID dataset where it obtains 100 % detection rate with an acceptable number of false positives.
A MATLAB version of the filtering steps and the dataset used in this paper will be freely available from http://www.dei.unipd.it/node/2357.
KeywordsFace detection Depth map Ensemble
- 2.C. Zhang, Z. Zhang, A survey of recent advances in face detection. Microsoft Research Technical Report, MSR-TR-2010-66, June 2010Google Scholar
- 4.H.L. Jin, Q.S. Liu, H.Q. Lu, Face detection using one-class based support vectors, in Proceedings 6th IEEE International Conference Automatic Face Gesture Recognition, Hoboken, NJ 07030 USA, (2004), pp. 457–462Google Scholar
- 5.P. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society conference on Computer Vision and Pattern Recognition, 1, 511--518 (2001)Google Scholar
- 9.M. Anisetti, Fast and robust face detection, in Multimedia Techniques for Device and Ambient Intelligence, Chapter 3 (Springer, US, 2009). ISBN: 978-0-387-88776-0Google Scholar
- 10.J. Li, Y. Zhang, Learning surf cascade for fast and accurate object detection, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2013) Hoboken, NJ 07030 USAGoogle Scholar
- 11.M. Mathias, et al. Face detection without bells and whistles, in Computer Vision–ECCV 2014 (Springer International Publishing, 2014), Zurich, Switzerland, pp. 720–735Google Scholar
- 13.R.I. Hg, P. Jasek, C. Rofidal, K. Nasrollahi, T.B. Moeslund, G. Tranchet, An RGB-D database using Microsoft’s Kinect for windows for face detection, in 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS) (IEEE, 2012), Hoboken, NJ 07030 USA, pp. 42–46Google Scholar
- 15.J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images. CVPR 2, 3 (2011)Google Scholar
- 16.R. Mattheij, E. Postma, Y. Van den Hurk, P. Spronck, Depth-based detection using Haarlike features, in Proceedings of the BNAIC 2012 Conference (Maastricht University, The Netherlands, 2012), pp. 162–169Google Scholar
- 17.M. Anisetti, V. Bellandi, E. Damiani, L. Arnone, B. Rat, A3FD: Accurate 3D face detection, in Signal Processing for Image Enhancement and Multimedia Processing (Springer, US, 2008), pp. 155–165Google Scholar
- 19.G. Goswami, S. Bharadwaj, M. Vatsa, R. Singh, On RGB-D face recognition using Kinect, in 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS) (IEEE, 2013), Hoboken, NJ 07030 USA, pp. 1–6Google Scholar
- 20.Y. Taigman, M. Yang, M.A., Ranzato, L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2014), Hoboken, NJ 07030 USA, pp. 1701–1708Google Scholar
- 21.O. Jesorsky, K. Kirchberg, R. Frischholz, in Face Detection Using the Hausdorff Distance, eds. by J. Bigun, F. Smeraldi. Audio and Video based Person Authentication—AVBPA, (Springer, 2001), Berlin, Germany, pp. 90–95Google Scholar
- 22.N. Markuš, M. Frljak, I.S. Pandžić, J. Ahlberg, R. Forchheimer, Fast localization of facial landmark points, in Proceedings of the Croatian Computer Vision Workshop, Zagreb, Croatia, (2014)Google Scholar
- 23.M. Nilsson, J. Nordberg, I. Claesson, Face detection using local SMQT features and split up SNOW classifier. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2, Hoboken, NJ 07030 USA, 589–592 (2007)Google Scholar
- 24.A. Asthana, S. Zafeiriou, S. Cheng, M. Pantic, Robust discriminative response map fitting with constrained local models, in 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2013), Hoboken, NJ 07030 USA, pp. 3444–3451Google Scholar
- 28.X. Tan, F. Song, Z.-H. Zhou, S. Chen, Enhanced pictorial structures for precise eye localization under incontrolled conditions, in IEEE Conference on Computer Vision and Pattern Recognition 2009 (CVPR 2009), Hoboken, NJ 07030 USA, 20–25 June 2009, pp. 1621, 1628Google Scholar
- 30.Z. Ren, J. Meng, J. Yuan. Depth camera based hand gesture recognition and its applications in human-computer-interaction, in Proceedings of ICICS (2011), Hoboken, NJ 07030 USA, pp. 1–5Google Scholar