ABSTRACT
This paper addresses the problem of privacy protection in face synthesis. We propose a new face synthesis approach based on tensor decomposition. By using the mathematical properties of tensor analysis, we decompose a face image into multiple factors so that the synthesis process could concentrate only on privacy related information. Then, we generate a new face image by altering the privacy related factors and keeping the other ones untouched. Compared to previous algorithms, our approach has the advantage in producing a synthetic face image without the risk of privacy leaking. We conduct the experiments in different datasets and factors to show the flexibility of the proposed approach. After gaining the synthesis images, we apply the automatic recognition algorithms for both expressions and faces to them. The experiment results demonstrate the effectiveness of our approach.
- Acquisti, A., Gross, R., and Stutzman, F. 2014. Face recognition and privacy in the age of augmented reality. Journal of Privacy and Confidentiality 6, 2.Google ScholarCross Ref
- Amos, B., Ludwiczuk, B., Harkes, J., Pillai, P., Elgazzar, K., and Satyanarayanan, M. Open-Face: Face Recognition with Deep Neural Networks. http://github.com/cmusatyalab/openface. Accessed: 2016-01-11.Google Scholar
- Bader, B. W., Kolda, T. G., et al., 2015. Matlab tensor toolbox version 2.6. Available online, February.Google Scholar
- Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., and Nayar, S. K. 2008. Face swapping: Automatically replacing faces in photographs. In In ACM Transactions on Graphics (Proceedings of SIGGRAPH. Google ScholarDigital Library
- Du, L., Yi, M., Blasch, E., and Ling, H. 2014. Attribute preserved face de-identification. In IEEE International Joint Conference on Biometrics, 1--8.Google Scholar
- Feng, Z.-H., Kittler, J., Christmas, W. J., Wu, X., and Pfeiffer, S. 2012. Automatic face annotation by multilinear aam with missing values. In ICPR, IEEE Computer Society, 2586--2589.Google Scholar
- Gross, R., Airoldi, E., Malin, B., and Sweeney, L. 2005. Integrating utility into face de-identification. In Privacy Enhancing Technologies, 5th International Workshop, PET 2005, Cavtat, Croatia, May 30-June 1, 2005, Revised Selected Papers, 227--242. Google ScholarDigital Library
- Gross, R., Sweeney, L., Cohn, J. F., la Torre, F. D., and Baker, S. 2008. Model-based de-identification of facial images. In AMIA 2008, American Medical Informatics Association Annual Symposium, Washington, DC, USA, November 8--12, 2008.Google Scholar
- Jourabloo, A., Yin, X., and Liu, X. 2015. Attribute preserved face de-identification. In Proc. 8th IAPR International Conference on Biometrics (ICB 2015).Google Scholar
- Kolda, T. G., and Bader, B. W. 2009. Tensor decompositions and applications. SIAM Rev. 51, 3 (Aug.), 455--500. Google ScholarDigital Library
- Lathauwer, L. D., Moor, B. D., and Vandewalle, J. 2000. On the best rank-1 and rank-(r1, r2,...,rn) approximation of higher-order tensor. SIAM Journal on Matrix Analysis and Applications, 21--1324. Google ScholarDigital Library
- Lee, H.-S., and Kim, D. 2009. Tensor-based aam with continuous variation estimation: Application to variation-robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 6, 1--1. Google ScholarDigital Library
- Mosaddegh, S., Simon, L., and Jurie, F. 2014. Photorealistic face de-identification by aggregating donors' face components. In Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1--5, 2014, Revised Selected Papers, Part III, 159--174.Google Scholar
- Newton, E. M., Sweeney, L., and Malin, B. 2005. Preserving privacy by de-identifying face images. IEEE Transactions on Knowledge and Data Engineering 17, 2, 232--243. Google ScholarDigital Library
- Nordstrøm, M. M., Larsen, M., Sierakowski, J., and Stegmann, M. B. 2004. The IMM face database - an annotated dataset of 240 face images. Tech. rep., Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, may.Google Scholar
- Ralph Gross, Latanya Sweeney, F. d. l. T., and Baker, S. 2008. Semi-supervised learning of multi-factor models for face de-identification. In IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
- Samarzija, B., and Ribaric, S. 2014. An approach to the de-identification of faces in different poses. In Information and Communication Technology, Electronics and Microelectronics(MIPRO), 1246 -- 1251.Google Scholar
- Schroff, F., Kalenichenko, D., and Philbin, J. 2015. Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Sim, T., Baker, S., and Bsat, M. 2001. The cmu pose, illumination, and expression (pie) database of human faces. Tech. Rep. CMU-RI-TR-01-02, Robotics Institute, Pittsburgh, PA, January.Google Scholar
- Vasilescu, M. A. O., and Terzopoulos, D. 2003. Multilinear subspace analysis of image ensembles. In CVPR (2), IEEE Computer Society, 93--99.Google Scholar
Index Terms
- A framework of face synthesis based on multilinear analysis
Recommendations
Unconstrained pose-invariant face recognition by a triplet collaborative dictionary matrix
We propose a novel method for real-world pose-invariant face recognition.Proposed method used from a single image in gallery with any facial expressions.We generate a collaborative dictionary matrix for each people.Promising results were obtained to ...
Pose-robust face recognition via sparse representation
We propose a pose-robust face recognition method to handle the challenging task of face recognition in the presence of large pose difference between gallery and probe faces. The proposed method exploits the sparse property of the representation ...
Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion
Analysis and Modeling of Faces and GesturesAbstractPose variance remains a challenging problem for face recognition. In this paper, a scheme including image synthesis and recognition is proposed to improve the performance of automatic face recognition system. In the image synthesis part, a series ...
Comments