Abstract
Human face images acquired using conventional 2D cameras may have inherent restrictions that hinder the inference of some specific information in the face. The low-cost depth sensors such as Microsoft Kinect introduced in late 2010 allow extracting directly 3D information, together with RGB color images. This provides new opportunities for computer vision and face analysis research. Although more accurate sensors for detailed facial image analysis are expected to be available soon (e.g. Kinect 2), this paper investigates the usefulness of the depth images provided by the current Microsoft Kinect sensors in different face analysis tasks. We conduct an in-depth study comparing the performance of the depth images provided by Microsoft Kinect sensors against RGB counterpart images in three face analysis tasks, namely identity, gender and ethnicity. Four local feature extraction methods are investigated for both face texture and shape description. Moreover, the two modalities (i.e. depth and RGB) are fused to gain insight into their complementarity. The experimental analysis conducted on two publicly available kinect face databases, EurecomKinect and Curtinfaces, yields into interesting results.
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
Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer, New York (2011)
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with Microsoft Kinect sensor: A review. IEEE Transactions on Cybernetics 43(5), 1318–1334 (2013)
Andersen, M., Jensen, T., Lisouski, P., Hansen, A., Gregersen, T., Ahrendt, P.: Kinect depth sensor evaluation for computer vision applications. Technical report, Department of Engineering, Aarhus University, Denmark (2012)
Fanelli, G., Weise, T., Gall, J., Van Gool, L.: Real time head pose estimation from consumer depth cameras. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 101–110. Springer, Heidelberg (2011)
Huynh, T., Min, R., Dugelay, J.-L.: An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 133–145. Springer, Heidelberg (2013)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Ahonen, T., Rahtu, E., Ojansivu, V., Heikkila, J.: Recognition of blurred faces using local phase quantization. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4, December 2008
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005
Kannala, J., Rahtu, E.: BSIF: Binarized statistical image features. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)
Li, B., Mian, A., Liu, W., Krishna, A.: Using Kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192, January 2013
Goswami, G., Bharadwaj, S., Vatsa, M., Singh, R.: On RGB-D face recognition using kinect. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6, September 2013
Min, R., Choi, J., Medioni, G., Dugelay, J.: Real-time 3D face identification from a depth camera. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1739–1742, November 2012
Pamplona Segundo, M., Sarkar, S., Goldgof, D., Silva, L., Bellon, O.: Continuous 3D face authentication using RGB-D cameras. In: IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 64–69 (2013)
Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 879–888, September 2006
Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 1927–1943 (2007)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008)
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 947–954 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Boutellaa, E., Bengherabi, M., Ait-Aoudia, S., Hadid, A. (2015). How Much Information Kinect Facial Depth Data Can Reveal About Identity, Gender and Ethnicity?. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8926. Springer, Cham. https://doi.org/10.1007/978-3-319-16181-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-16181-5_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16180-8
Online ISBN: 978-3-319-16181-5
eBook Packages: Computer ScienceComputer Science (R0)