EvoFIT: A holistic, evolutionary facial imaging technique for creating composites

EvoFIT, a computerized facial composite system is being developed as an alternative to current systems. EvoFIT faces are initially presented to a witness with random characteristics, but through a process of selection and breeding, a composite is "evolved." Comparing composites constructed with E-FIT, a current system, a naming rate of 10% was found for EvoFIT and 17% for E-FIT. Analysis revealed that target age was limiting factor for EvoFIT and a second study with age-appropriate targets visible during composite construction produced a naming rate similar to E-FIT. Two more-realistic studies were conducted that involved young target faces and two current systems (E-FIT and PROfit). Composites from both of these experiments were poorly named but a significant benefit emerged for EvoFIT.

[1]  S. Penrod,et al.  Meta-analysis of facial identification studies. , 1986 .

[2]  Margaret Bull Kovera,et al.  Identification of computer-generated facial composites. , 1997, The Journal of applied psychology.

[3]  Charles Hulme,et al.  Programmed cell suicide in the developing nervous system: A functional neural network model. , 1994 .

[4]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[5]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Peter J. B. Hancock,et al.  Testing Principal Component Representations for Faces , 1997, NCPW.

[7]  V. Bruce,et al.  A comparison of two computer-based face identification systems with human perceptions of faces , 1998, Vision Research.

[8]  V. Bruce,et al.  Recognition of unfamiliar faces , 2000, Trends in Cognitive Sciences.

[9]  R. Edward Geiselman,et al.  Building composite facial images: effects of feature saliency and delay of construction , 1989 .

[10]  Ian Craw,et al.  Parameterising Images for Recognition and Reconstruction , 1991, BMVC.

[11]  S Hollander,et al.  Recognition memory for typical and unusual faces. , 1979, Journal of experimental psychology. Human learning and memory.

[12]  Timothy F. Cootes,et al.  View-based active appearance models , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[13]  V. Bruce,et al.  Face processing: Human perception and principal components analysis , 1996, Memory & cognition.

[14]  Baruch Cahlon,et al.  To catch a thief with a recognition test: The model and some empirical results , 1989, Cognitive Psychology.

[15]  V. Bruce,et al.  The importance of ‘mass’ in line drawings of faces , 1992 .

[16]  Charlie David Frowd,et al.  EvoFIT: A Holistic, Evolutionary Facial Imaging System , 2002 .

[17]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[18]  Felicity Gibling,et al.  Artistic enhancement in the production of photo-fit likenesses: An examination of its effectiveness in leading to suspect identification , 1994 .

[19]  Vicki Bruce,et al.  Four heads are better than one: combining face composites yields improvements in face likeness. , 2002, The Journal of applied psychology.

[20]  P J Hancock,et al.  Evolving faces from principal components , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[21]  A. O'Toole,et al.  Three-Dimensional Caricatures of Human Heads: Distinctiveness and the Perception of Facial Age , 1997, Perception.

[22]  T. Valentine,et al.  Towards an Exemplar Model of Face Processing: The Effects of Race and Distinctiveness , 1992, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[23]  Graham Davies,et al.  The impact of character attribution on composite production: A real world effect? , 1999 .

[24]  G Davies,et al.  Facial composite production: a comparison of mechanical and computer-driven systems. , 2000, The Journal of applied psychology.

[25]  Craig Caldwell,et al.  Tracking a Criminal Suspect Through "Face-Space" with a Genetic Algorithm , 1991, ICGA.