Ranran Feng
ABSTRACT
Artists and fashion designers have recently been creating a new form of art -- Camouflage Art -- which can be used to prevent computer vision algorithms from detecting faces. This digital art technique combines makeup and hair styling, or other modifications such as facial painting to help avoid automatic face-detection. In this paper, we first study the camouflage interference and its effectiveness on several current state of art techniques in face detection/recognition; and then present a tool that can facilitate digital art design for such camouflage that can fool these computer vision algorithms. This tool can find the prominent or decisive features from facial images that constitute the face being recognized; and give suggestions for camouflage options (makeup, styling, paints) on particular facial features or facial parts. Testing of this tool shows that it can effectively aid the artists or designers in creating camouflage-thwarting designs. The evaluation on suggested camouflages applied on 40 celebrities across eight different face recognition systems (both non-commercial or commercial) shows that 82.5% ~ 100% of times the subject is unrecognizable using the suggested camouflage.
- Harvey, A. 2010. CV Dazzle: Camouflage from Computer Vision. Available at: http://cvdazzle.com/Google Scholar
- Viola, P., and Jones, M. J. 2004. Robust Real-Time Face Detection. Int. J. Comput. Vision57, 2 (May 2004), 137--154. Google ScholarDigital Library
- Sky Biometry. Available at: http://www.skybiometry.com/Google Scholar
- Ramanathan, N., Chowdhury, A., and Chellappa, R. 2004. Facial similarity across age, disguise, illumination and pose, in Proceedings of International Conference on Image Processing, 3, 1999--2002.Google Scholar
- Singh, R., Vatsa, M., and Noore A., 2009. Face recognition with disguise and single gallery images. Image and Vision Computing, 27, 3, 245--257. Google ScholarDigital Library
- Phillips, J., Todd Scruggs, W., O'Toole, A.J., Flynn, P. J., Bowyer, K.W., Schott, C.L., and Sharpe, M. 2007. FRVT 2006 and ICE 2006 Large-Scale Results. NISTIR 7408.Google Scholar
- DIS Magazine, 2013. How to Hide from Machines. Available at: http://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-frommachines/Google Scholar
- Davis, G., and Hall, M. 2012. The Makeup Artist Handbook: Techniques for Film, Television, Photography, and Theatre. Taylor & Francis US.Google Scholar
- Luckiesh, M. 1922. Visual Illusion: Their Causes, Characteristics and Applications. D. Van Nostrand Company, New York.Google ScholarCross Ref
- Phan, M. 2012. Michelle Phan Tutorials. Available at: http://michellephan.com/blog/cat/tutorialsGoogle Scholar
- Google+/Google Picasa Face Recognition. 2012.Google Scholar
- Baidu Face Search. 2013. Available at: http://stu.baidu.com/Google Scholar
- Duda, R. O., Hart, P. E., and Stork, D. G. 2001. Pattern Classification, Wiley Interscience. Google ScholarDigital Library
- Sirovich, L., Kirby, M. 1987. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America. 4 (3): 519--524.Google ScholarCross Ref
- Belhumeur, P., Hespanha, J., and Kriegman, D. 1997. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720. Google ScholarDigital Library
- Brunelli, R., and Poggio, T. 1993. "Face Recognition: Features vs. Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, 15(10): 1042--1053. Google ScholarDigital Library
- Kanade, T. 1973. Picture Processing System by Computer Complex and Recognition of Human Faces. doctoral dissertation, Kyoto University.Google Scholar
- Brunelli, R., Poggio, T. 1993. Face recognition: features versus templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10): 1042--1052. Google ScholarDigital Library
- Edwards, G. J., Cootes, T. F., Taylor, C. J. 1998. Face Recognition using Acitive Appearance Models. In Proceedings of 5th European Conference on Computer Vision (ECCV '98), 2: 581--595. Google ScholarDigital Library
- Cox, I.J., Ghosn, J., and Yianilos, P.N. 1996. Feature-based face recognition using mixture-distance. IEEE Conference on Computer Vision and Pattern Recognition (CVPR '96), 209--216. Google ScholarDigital Library
- Wiskott, L., Fellous, J.-M., Kruger, N., and Von der Malsburg, C. 1997. Face recognition by elastic bunch graph matching. International Conference on Image Processing, 1:129--132. Google ScholarDigital Library
- Craw, I., Tock, D., and Bennett, A. 1992. Finding Face Features. In Proceedings of 2nd European Conference on Computer Vision (ECCV '92), Springer-Verlag, London, UK, 92--96. Google ScholarDigital Library
- Ahonen, T., Hadid, A., and Pietikainen, M. 2006. Face Description with Local Binary Patterns: Application to Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 2037--2041. Google ScholarDigital Library
- Liu, C., and Wechsler, H. 2003. Independent component analysis of Gabor features for face recognition, IEEE Transactions on Neural Networks, 14(4): 919--928. Google ScholarDigital Library
- Anderson, I. 2008. 1940s Hairstyle Book by Anderson (Vintage Hairstyling). Bramcost Publications.Google Scholar
- Animal Painted on Lips. 2011. Available at: http://toxel.com/Google Scholar
- PicTriev. 2013. Available at: http://www.pictriev.comGoogle Scholar
- Chiang, P., Liao, W., Li, T. 2004. Automatic Caricature Generation by Analyzing Facial Features, in Proceeding of 2004 Asia Conference on Computer Vision (ACCV2004).Google Scholar
- Cootes, T.F., Edwards, G.J,; and Taylor, C.J., Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6): 681--685. Google ScholarDigital Library
- Milborrow, S., Morkel, J., and Nicolls, F. 2010. The MUCT Landmarked Face Database, Pattern Recognition Association of South Africa.Google Scholar
Index Terms
- Facilitating fashion camouflage art
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