Abstract
Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. Most existing methods estimate head poses that are included in the training data (i.e., previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution (MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing’04 database, the mean absolute errors of results for yaw and pitch are 4.01° and 2.13°, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.
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References
Aghajanian, J., Prince, S.J.D., 2009. Face pose estimation in uncontrolled environments. Proc. British Machine Vision Conf., p.1–11.
Berger, A.L., Pietra, V.J.D., Pietra, S.A.D., 1996. A maximum entropy approach to natural language processing. Comput. Ling., 22(1): 39–71.
Bowyer, K.W., Chang, K., Flynn, P., 2006. A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Understand., 101(1): 1–15. http://dx.doi.org/10.1016/j.cviu.2005.05.005
Brunelli, R., 1997. Estimation of pose and illuminant direction for face processing. Image Vis. Comput., 15(10): 741–748. http://dx.doi.org/10.1016/S0262-8856(97)00024-3
Cai, Y., Yang, M.L., Li, Z.Q., 2015. Robust head pose estimation using a 3D morphable model. Math. Prob. Eng., 2015:678973.1–678973.10. http://dx.doi.org/10.1155/2015/678973
Do, M.N., 2003. Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models. IEEE Signal Process. Lett., 10(4): 115–118. http://dx.doi.org/10.1109/LSP.2003.809034
Fenzi, M., Leal-Taixé, L., Rosenhahn, B., et al., 2013. Class generative models based on feature regression for pose estimation of object categories. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.755–762.
Fitzpatrick, P., 2000. Head Pose Estimation Without Manual Initialization. Report, Massachusetts Institute of Technology, Cambridge.
Gao, W., Cao, B., Shan, S.G., et al., 2008. The CASPEAL large-scale Chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. A, 38(1): 149–161. http://dx.doi.org/10.1109/TSMCA.2007.909557
Geng, X., Xia, Y., 2014. Head pose estimation based on multivariate label distribution. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1837–1842.
Gourier, N., Hall, D., Crowley, J.L., 2004. Estimating face orientation from robust detection of salient facial features. Proc. Int. Workshop on Visual Observation of Deictic Gestures. Available from http://www-prima. inrialpes.fr/perso/Gourier/Faces/HPDatabase.html.
Gross, R., Matthews, I., Cohn, J., et al., 2010. Multi-PIE. Image Vis. Comput., 28(5): 807–813. http://dx.doi.org/10.1016/j.imavis.2009.08.002
Haj, M.A., Gonzà lez, J., Davis, L.S., 2012. On partial least squares in head pose estimation: how to simultaneously deal with misalignment. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2602–2609. http://dx.doi.org/10.1109/CVPR.2012.6247979
Hu, C.L., Gong, L.Y., Wang, T.J., et al., 2014. An effective head pose estimation approach using Lie algebrized Gaussians based face representation. Multim. Tools Appl., 73(3): 1863–1884. http://dx.doi.org/10.1007/s11042-013-1676-5
Huang, D., Storer, M., de la Torre, F., et al., 2011. Supervised local subspace learning for continuous head pose estimation. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2921–2928. http://dx.doi.org/10.1109/CVPR.2011.5995683
Jain, V., Crowley, J.L., 2013. Head pose estimation using multi-scale Gaussian derivatives. Proc. 18th Scandinavian Conf. on Image Analysis, p.319–328. http://dx.doi.org/10.1007/978-3-642-38886-6_31
Krüger, V., Sommer, G., 2002. Gabor wavelet networks for efficient head pose estimation. Image Vis. Comput., 20(9-10):665–672. http://dx.doi.org/10.1016/S0262-8856(02)00056-2
Liu, D.C., Nocedal, J., 1989. On the limited memory BFGS method for large scale optimization. Math. Program., 45(1): 503–528. http://dx.doi.org/10.1007/BF01589116
Lu, F., Sugano, Y., Okabe, T., et al., 2012. Head pose-free appearance-based gaze sensing via eye image synthesis. Proc. 21st Int. Conf. on Pattern Recognition, p.1008–1011.
Lu, F., Okabe, T., Sugano, Y., et al., 2014. Learning gaze biases with head motion for head pose-free gaze estimation. Image Vis. Comput., 32(3): 169–179. http://dx.doi.org/10.1016/j.imavis.2014.01.005
Ma, B.P., Chai, X.J., Wang, T.J., 2013. A novel feature descriptor based on biologically inspired feature for head pose estimation. Neurocomputing, 115:1–10. http://dx.doi.org/10.1016/j.neucom.2012.11.005
Ma, B.P., Li, A.N., Chai, X.J., et al., 2014. CovGa: a novel descriptor based on symmetry of regions for head pose estimation. Neurocomputing, 143:97–108. http://dx.doi.org/10.1016/j.neucom.2014.06.014
Ma, B.P., Huang, R., Qin, L., 2015. VoD: a novel image representation for head yaw estimation. Neurocomputing, 148:455–466. http://dx.doi.org/10.1016/j.neucom.2014.07.019
Ma, X.H., Tan, Y.Q., Zheng, G.M., 2013. A fast classification scheme and its application to face recognition. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(7): 561–572. http://dx.doi.org/10.1631/jzus.CIDE1309
Murphy-Chutorian, E., Trivedi, M.M., 2009. Head pose estimation in computer vision: a survey. IEEE Trans. Patt. Anal. Mach. Intell., 31(4): 607–626. http://dx.doi.org/10.1109/TPAMI.2008.106
Pang, H., Lin, A., Holford, M., et al., 2006. Pathway analysis using random forests classification and regression. Bioinformatics, 22(16): 2028–2036. http://dx.doi.org/10.1093/bioinformatics/btl344
Sim, T., Baker, S., Bsat, M., 2002. The CMU pose, illumination, and expression (PIE) database. Proc. 5th IEEE Int. Conf. on Automatic Face and Gesture Recognition, p.46–51. http://dx.doi.org/10.1109/AFGR.2002.1004130
Tang, Y.Q., Sun, Z.N., Tan, T.N., 2014. A survey on head pose estimation. Patt. Recogn. Artif. Intell., 27(3): 213–225.
Wu, J.W., Trivedi, M.M., 2008. A two-stage head pose estimation framework and evaluation. Patt. Recog., 41(3): 1138–1158. http://dx.doi.org/10.1016/j.patcog.2007.07.017
Zhang, Z.P., Luo, P., Loy, C.C., et al., 2014. Facial landmark detection by deep multi-task learning. Proc. 13th European Conf. on Computer Vision, p.94–108. http://dx.doi.org/10.1007/978-3-319-10599-4_7
Zhu, R.H., Sang, G.L., Cai, Y., et al., 2013. Head pose estimation with improved random regression forests. Proc. 8th Chinese Conf. on Biometric Recognition, p.457–465. http://dx.doi.org/10.1007/978-3-319-02961-0_57
Zhu, X.X., Ramanan, D., 2012. Face detection, pose estimation, and landmark localization in the wild. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2879–2886. http://dx.doi.org/10.1109/CVPR.2012.6248014
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Project supported by the National Key Scientific Instrument and Equipment Development Project of China (No. 2013YQ49087903) and the National Natural Science Foundation of China (No. 61202160)
ORCID: Gao-li SANG, http://orcid.org/0000-0002-6567-1652
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Sang, Gl., Chen, H., Huang, G. et al. Unseen head pose prediction using dense multivariate label distribution. Frontiers Inf Technol Electronic Eng 17, 516–526 (2016). https://doi.org/10.1631/FITEE.1500235
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DOI: https://doi.org/10.1631/FITEE.1500235