Synthesis of photographic quality facial composites using evolutionary algorithms
Inhaltsverzeichnis
Reference
Gibson, S.J., Solomon, C.J., Pallares-Bejarano, A. (2003). Synthesis of photographic quality facial composites using evolutionary algorithms. In Harvey, R., Bangham, J.A., eds.: British Machine Vision Conference, 221–230.
DOI
http://dx.doi.org/10.5244/C.17.23
Abstract
A facial composite system is described for use in criminal investigations which has distinct advantages over current methods. Unlike traditional fea- ture based methods, our approach uses both local and global facial mod- els, allowing a witness to evolve plausible, photo-realistic face images in an intuitive way. The basic method combines random sampling from a facial appearance model (AM) with an evolutionary algorithm (EA) to drive the search procedure to convergence. Three variants of the evolutionary algo- rithm have been explored and their performance measured using a computer simulation of a human witness (virtual witness). Further system functional- ity, provided by local appearance models and transformations of the appear- ance space which respectively allow both local features and semantic facial attributes to be manipulated, is presented. Preliminary examples of compos- ites generated with our system are presented which demonstrate the potential superiority of the evolutionary approach to composite generation.
Extended Abstract
Bibtex
Used References
[1] ABM. Profit. http://www.abm-uk.com/.
[2] A.W.Young, D. Hellawell, and D.C. Hay. Configurational information in face per- ception. Perception, 16:747–749, 1987.
[3] A.D. Baddeley. The psychology of memory. Harper and Row, 1976.
[4] J.C. Bartlett and J. Searcy. Inversion and configuration of faces. Cognitive Psychol- ogy, 25:281–316, 1993.
[5] V. Blanz and T. Vetter. A morphable model for the synthesis of 3d faces. In SIG- GRAPH’99 Conference Proceedings, pages 187–194, 1999.
[6] V. Bruce, P. Hancock, and A. M. Burton. Human Face Perception and Identification, in Face Recognition - From Theory to Applications, pages 51–72. Springer Verlag, 1998. ISBN3-540-64410-5.
[7] R. Brunelli and O. Mich. Spotit! an interactive identikit system. Technical report, IRST Technical Report, 1994.
[8] T. Cootes and C. Taylor. Statistical models of appearance for medical image analysis and computer vision. In SPIE Medical Imaging, pages 236–248, 2001.
[9] G.M. Davies and D. Christie. Face recall: An examination of some factors limiting composite production accuracy. Journal of Applied Psychology, 67:103–109, 1982.
[10] H.D. Ellis, G.M. Davies, and J.W. Shepherd. A critical examination of the photofit system for recalling faces. Ergonomics, 21:297–307, 1978.
[11] A.J. O’Toole et al. Structural aspects of face recognition and the other-race effect. Memory and Cognition, 22:208–224, 1994.
[12] P.J.B Hancock. Evolving faces from principal components. Behaviour Research Methods, Instruments and Computers, 32(2):327–333, 2000.
[13] D. Hines, L. Jordan-Brown, and K.R. Juzwin. Hemispheric visual processing in face recognition. brain and cognition. Brain Cognition, 6:91–100, 1987.
[14] V.S. Johnston, C.J. Solomon, S.J. Gibson, and A. Pallares-Bejarano. Human fa- cial beauty: Current theories and methodologies. AMA Archives of Plastic Surgery, Manuscript QSP20000, 2003. in press.
[15] Aspley Ltd. E-fit. http://www.efit.co.uk/.
[16] L. Sirovich and M. Kirby. Low dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 4(3):519–524, 1987.
[17] J.W. Tanaka and M.J. Farah. Parts and wholes in face recognition. Quarterly Journal of Experimental Psychology, 46A:225–245, 1993.
[18] J. Turner, G. Pike, N. Towell, R. Kemp, and P. Bennett. Making faces: Comparing e-fit construction techniques. Proceedings of The British Psychological Society, 7(1):78, 1999.
Links
Full Text
http://www.kent.ac.uk/physical-sciences/staff/cjs-impact/Kent_journal_publications/BMVC2003.pdf
