Abstract
Driver head analysis is of paramount interest for the advanced driver assistance systems (ADAS). Recently proposed methods almost rely on training with labeled samples, especially deep learning. However, the labeling process is a subjective and tiresome manual task. Even trickier, our application scene is driver assistance systems, where the training dataset is more difficult to capture. In this paper, we present a rendering pipeline to synthesize virtual-world driver head pose and facial landmark dataset with annotation by computer 3D animation software, in which we consider driver’s gender, dress, hairstyle, hats and glasses. This large amounts of virtual-world labeled dataset and a small amount of real-world labeled dataset are trained together firstly by deeply supervised transfer metric learning method. We treat it as a cross-domain task, the labeled virtual data is a source domain and the unlabeled real-world data is a target domain. By exploiting the feature self-learning characteristic of deep networks, we find the common feature subspace between them, and transfer discriminative knowledge from the labeled source domain to the labeled target domain. Finally we employ a small number of real-world dataset to fine-tune the model iteratively. Our experiments show high accuracy on real-world driver head images.
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References
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 607–626 (2009)
Papazov, C., Marks, T.K., Jones, M.: Real-time 3D head pose and facial landmark estimation from depth images using triangular surface patch features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4722–4730 (2015). https://doi.org/10.1109/cvpr.2015.7299104
Lee, D., Yang, M.H., Oh, S.: Fast and accurate head pose estimation via random projection forests. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1958–1966 (2015). https://doi.org/10.1109/iccv.2015.227
Blake, A., Isard, M.: Active shape models. In: Active Contours, pp. 25–37. Springer, London (1998). https://doi.org/10.1007/978-1-4471-1555-7_2
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Li, Y., Feng, J., Meng, L., Wu, J.: Sparse representation shape models. J. Math. Imaging Vis. 48(1), 83–91 (2014). https://doi.org/10.1007/s10851-012-0394-3
Lee, Y.H., Han, W., Kim, Y., Kim, B.: Facial feature extraction using an active appearance model on the iPhone. In: 2014 Eighth International Conference on IEEE Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 196–201 (2014). https://doi.org/10.1109/imis.2014.24
Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2), 177–190 (2014). https://doi.org/10.1007/s11263-013-0667-3
Dong, Z., Wu, Y., Pei, M., Jia, Y.: Vehicle type classification using a semisupervised convolutional neural network. IEEE Trans. Intell. Transp. Syst. 16(4), 2247–2256 (2015)
Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Tang, X.: Deepid-net: deformable deep convolutional neural networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2015). https://doi.org/10.1109/cvpr.2015.7298854
Dong, Z., Jia, S., Wu, T., Pei, M.: Face video retrieval via deep learning of binary hash representations. In: AAAI, pp. 3471–3477 (2016)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013). https://doi.org/10.1109/cvpr.2013.446
Wu, Y., Wang, Z., Ji, Q.: Facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3452–3459 (2013). https://doi.org/10.1109/cvpr.2013.443
Marin, J., Vazquez, D., Geronimo, D., Lopez, A.M.: Learning appearance in virtual scenarios for pedestrian detection. In: Computer Vision and Pattern Recognition (2010). https://doi.org/10.1109/cvpr.2010.5540218
Shotton, J., Sharp, T., Kipman, A.A., Fitzgibbon, A., Finocchio, M.J., Blake, A., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013). https://doi.org/10.1109/cvpr.2011.5995316
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M.J., Blake, A.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2821–2840 (2013). https://doi.org/10.1109/iccv.2013.429
Vazquez, D., Lopez, A.M., Marin, J., Ponsa, D., Geronimo, D.: Virtual and real world adaptation for pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(4), 797–809 (2014). https://doi.org/10.1109/tpami.2013.163
Hu, J., Lu, J., Tan, Y.P.: Deep transfer metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 325–333 (2015). https://doi.org/10.1109/cvpr.2015.7298629
Zhang, J., Shan, S., Kan, M., Chen, X.: Coarse-to-Fine Auto-Encoder Networks (CFAN) for real-time face alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 1–16. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_1
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014). https://doi.org/10.1109/cvpr.2014.241
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This work was supported in part by the National Nature Science Foundation of China under Grant no 61672286 and 61673220.
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Liu, K., Liu, Y., Sun, Q., Pranata, S., Shen, S. (2018). Driver Head Analysis Based on Deeply Supervised Transfer Metric Learning with Virtual Data. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_28
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