Abstract
Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and characteristic lengths of the body. In the typical approach, person re-identification is performed using appearance, thus invalidating any application in which a person may change dress across subsequent acquisitions. For example, this is a relevant scenario for home patient monitoring. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. We propose a model to incorporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method improves rank-1 accuracy especially on short realistic sequences.
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Notes
- 1.
The Florence 3D Re-Id dataset is released for public use at the following link http://www.micc.unifi.it/.
References
Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, pp. 649–656, June 2011
Lisanti, G., Masi, I., Bagdanov, A., Del Bimbo, A.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1629–1642 (2015)
Velardo, C., Dugelay, J.: Improving identification by pruning: a case study on face recognition and body soft biometric. In: International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Dublin, Ireland, pp. 1–4, May 2012
Barbosa, I.B., Cristani, M., Del Bue, A., Bazzani, L., Murino, V.: Re-identification with RGB-D sensors. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7583, pp. 433–442. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33863-2_43
Pala, F., Satta, R., Fumera, G., Roli, F.: Multimodal person re-identification using RGB-D cameras. IEEE Trans. Circuits Syst. Video Technol. 26(4), 788–799 (2016)
Baltieri, D., Vezzani, R., Cucchiara, R.: Mapping appearance descriptors on 3D body models for people re-identification. Int. J. Comput. Vis. 111(3), 345–364 (2014)
Munaro, M., Basso, A., Fossati, A., Gool, L.V., Menegatti, E.: 3D reconstruction of freely moving persons for re-identification with a depth sensor. In: IEEE International Conference on Robotics and Automation (ICRA), Hong-Kong, pp. 4512–4519, May 2014
Berretti, S., Pala, P., Del Bimbo, A.: Face recognition by super-resolved 3D models from consumer depth cameras. IEEE Trans. Inf. Forensics Secur. 9(9), 1436–1449 (2014)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of the International Conference on 3D Digital Imaging and Modeling (3DIM), Quebec City, Canada, pp. 145–152, May 2001
Ferrari, C., Lisanti, C., Berretti, S., Del Bimbo, A.: Dictionary learning based 3D morphable model construction for face recognition with varying expression and pose. In: International Conference on 3D Vision (3DV), Lion, France, pp. 509–517, October 2015
Berretti, S., Del Bimbo, A., Pala, P.: Sparse matching of salient facial curves for recognition of 3D faces with missing parts. IEEE Trans. Inf. Forensics Secur. 8(2), 374–389 (2013)
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, M., Kohli, P., Criminisi, A., Kipman, A., et al.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2821–2840 (2013)
Rafi, U., Gall, J., Leibe, B.: A semantic occlusion model for human pose estimation from a single depth image. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 67–74, June 2015
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Bondi, E., Pala, P., Seidenari, L., Berretti, S., Del Bimbo, A. (2018). Long Term Person Re-identification from Depth Cameras Using Facial and Skeleton Data. In: Wannous, H., Pala, P., Daoudi, M., Flórez-Revuelta, F. (eds) Understanding Human Activities Through 3D Sensors. UHA3DS 2016. Lecture Notes in Computer Science(), vol 10188. Springer, Cham. https://doi.org/10.1007/978-3-319-91863-1_3
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