Emotion recognition from scrambled facial images via many graph embedding

Pattern Recognition

Volumn 67, Issue , 2017, Pages 245-251

  Jiang, Richard ^a   Ho, Anthony T S ^b   Cheheb, Ismahane ^a   Al Maadeed, Noor ^c   Al Maadeed, Somaya ^c   Bouridane, Ahmed ^a  

^a NORTHUMBRIA UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c QATAR UNIVERSITY   (Qatar)

Author keywords

Chaotic patterns; Emotion recognition; Facial expression; Many graph embedding; User privacy

Indexed keywords

BENCHMARKING; COMPUTER VISION; DIAGNOSIS; HUMAN COMPUTER INTERACTION; INTERNET OF THINGS; PATTERN RECOGNITION; SEMANTICS; SPEECH RECOGNITION;

CHAOTIC PATTERN; EMOTION RECOGNITION; FACIAL EXPRESSIONS; GRAPH EMBEDDINGS; USER PRIVACY;

FACE RECOGNITION;

EID: 85016085782     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2017.02.003     Document Type: Article

References (42)

1. [1] Riek, L.D., Watson, R.N.M., The age of avatar realism. IEEE Rob. Autom. Mag. 17:4 (2010), 37–42.
2. [2] Jayatilake, D., Isezaki, T., Teramoto, Y., Eguchi, K., Suzuki, K., Robot assisted physiotherapy to support rehabilitation of facial paralysis. IEEE Trans. Neural Syst. Rehab. Eng. 22:3 (2014), 644–653.
3. [3] Balasubramanian, Y., Sivasankaran, K., Krishraj, S.P., Forensic video solution using facial feature-based synoptic video footage record. IET Comput. Vision 10:4 (2016), 315–320.
4. [4] McDuff, D., Kaliouby, R.E., Picard, R.W., Crowdsourcing facial responses to online videos. IEEE Trans. Affect. Comput. 3:4 (2012), 456–468.
5. [5] Fleck, S., Strasser, W., Smart camera based monitoring system and its application to assisted living. Proc. IEEE, 96(10), Oct. 2008 On page(s): 1698–1714.
6. [6] Melle, A., Dugelay, J.-L., Scrambling faces for privacy protection using background self-similarities. Proc. 2014 IEEE International Conference on Image Processing (ICIP), 2014, 6046–6050.
7. [7] Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T., Privacy-preserving face recognition. Proc. Ninth Int'l Symp. Privacy Enhancing Technologies (PETS ’09), 2009, 235–253.
8. [8] Honda, T., Murakami, Y., Yanagihara, Y., Kumaki, T., Fujino, T., Hierarchical image-scrambling method with scramble-level controllability for privacy protection. Proc. IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), 2013, 1371–1374.
9. [9] Erdlyi, A., Bart, T., Valet, P., Winkler, T., Rinner, B., Adaptive cartooning for privacy protection in camera networks. Proc. International Conference on Advanced Video and Signal Based Surveillance, 2014, 6.
10. [10] Dufaux, F., Video scrambling for privacy protection in video surveillance: recent results and validation framework. Proc. SPIE, 8063, 2011, 14.
11. [11] Winkler, T., Rinner, B., Security and privacy protection in visual sensor networks: a survey. ACM Comput. Surv., 47(42), 2014, 1.
12. [12] Erdlyi, A., Bart, T., Valet, P., Winkler, T., Rinner, B., Adaptive cartooning for privacy protection in camera networks. Proc. International Conference on Advanced Video and Signal Based Surveillance, 2014, 6.
13. [13] Wang, Y., Li, T., Study on image encryption algorithm based on arnold transformation and chaotic system. Proc. 2010 International Conference on Intelligent System Design & Engineering Application, 2010, 449–451.
14. [14] Tang, Z., Zhang, X., Secure image encryption without size limitation using arnold transform and random strategies. J. Multimed. 6:2 (April 2011), 202–206.
15. [15] Mariooryad, S., Busso, C., Exploring cross-modality affective reactions for audiovisual emotion recognition. IEEE Trans. Affect. Comput. 4:2 (2013), 183–196.
16. [16] Zheng, Wenming, Multi-View facial expression recognition based on group sparse reduced-rank regression. IEEE Trans. Affect. Comput. 5:1 (2014), 71–85.
17. [17] Li, Yongqiang, Wang, Shangfei, Zhao, Yongping, Ji, Qiang, Simultaneous facial feature tracking and facial expression recognition. IEEE Trans. Image Process. 22:7 (2013), 2559–2573.
18. [18] Jiang, Bihan, Valstar, M., Martinez, B., Pantic, M., A dynamic appearance descriptor approach to facial actions temporal modeling. IEEE Trans. Cybern. 44:2 (2014), 161–174.
19. [19] Ekman, P., Friesen, W., Facial Action Coding System: A Technique For the Measurement of Facial Movement. 1978, Consulting Psychologists Press, Palo Alto.
20. [20] Perakis, P., Passalis, G., Theoharis, T., Kakadiaris, I.A., 3D facial landmark detection under large yaw and expression variations. IEEE Trans. Pattern Anal. Mach. Intell. 35:7 (2013), 1552–1564.
21. [21] Hsu, R., Jain, A., Semantic face matching. IEEE International Conference on Multimedia and Expo, 2002, 145.
22. [22] Cootes, T.F., Edwards, G.J., Taylor, C.J., Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell., 23(6), 2001, 681.
23. [23] Taheri, S., Patel, V.M., Chellappa, R., Component-based recognition of faces and facial expressions. IEEE Trans. Affect. Comput. 4:4 (2013), 360–371.
24. [24] Rahulamathavan, Y., Phan, R.C.-W., Chambers, J.A., Parish, D.J., Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Tran. Affect. Comput. 4:1 (2013), 83–92.
25. [25] Jiang, R., Al-Maadeed, S., Bouridane, A., Crookes, D., Celebi, M.E., Face recognition in the scrambled domain via salience-aware ensembles of many kernels. IEEE Trans. Inf. Forensics Security 11:8 (Aug 2016), 1807–1817.
26. [26] P. Liu, S. Han, Z. Meng, Y. Tong, Facial expression recognition via a boosted deep belief network, CVPR 2014.
27. [27] Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y., Robust Face Recognition via Sparse Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 31:2 (Feb.2009), 210–227 Also see CVPR 2014.
28. [28] Chen, D., Cao, X., Wen, F., Sun, J., Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. Computer Vision and Pattern Recognition (CVPR), 2013.
29. [29] Draper, B., Baek, K., Bartlett, M., Beveridge, J., Recognizing faces with PCA and ICA. Comput. Vis. Image Understand., 91(1-2), 2003, 115.
30. [30] Yang, M.H., Kernel eigenfaces vs. kernel Fisherface: face recognition using kernel methods. International Conference on Automatic Face and Gesture Recognition, 2002, 215.
31. [31] Tenenbaum, B., Silva, V., Langford, J., A global geometric framework for nonlinear dimensionality. Science, 290(5500), 2000, 2319.
32. [32] Belkin, M., Niyogi, P., Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, 2001, 14.
33. [33] He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J., Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell., 27(3), Mar. 2005, 1.
34. [34] He, X., Deng, C., Niyogi, P., Tensor subspace analysis. Advances in Neural Information Processing Systems 18 (NIPS), Vancouver, Canada, Dec. 2005.
35. [35] D. Foster, S. Kakade, T. Zhang, Multi-view dimensionality reduction via canonical correlation analysis, TTI-C Technical Report, TTI-TR-2008-4, 2008.
36. [36] Hedge et al., Random projections for manifold learning, NIPS 2008.
37. [37] Hou, C., Zhang, C., Wu, Y., et al. Multiple view semi-supervised dimensionality reduction. Pattern Recognit. 43:3 (2010), 720–730.
38. [38] Xia, T., Tao, D., Mei, T., Zhang, Y., Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B 40:6 (2010), 1438–1446.
39. [39] Han, Y., Wu, F., Tao, D., Shao, J., Zhuang, Y., Jiang, J., Sparse unsupervised dimensionality reduction for multiple view data. IEEE Trans. Circuits Syst. Video Technol. 22:10 (2012), 1485–1496.
40. [40] Lyons, M., Budynek, J., Akamatsu, S., Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21:12 (Dec. 1999), 1357–1362.
41. [41] Aifanti, N., Delopoulos, A., Linear subspaces for facial expression recognition. Signal Process. Image Commun. 29:1 (2014 Jan), 177–188.
42. [42] P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, I. Matthews, The extended Cohn–Kanade dataset (CK+): a complete expression dataset for action unit and emotion-specified expression, Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis, San Francisco, USA, 94–101.