Creation of Facial Composites from User Selections using Image Gradient

Rubén García-Zurdo

Abstract


Evolutionary facial composites are created using interactive genetic algorithms based on user selections. This approach is grounded in perceptive studies, and is superior to feature-based systems. A method is presented for creating facial composites in which faces are encoded with shape information, the coordinates of a predefined landmark points, and the image gradient, which represents face information more precisely than image luminance. The new method is accompanied by a Poisson integration process that presents the user with candidate faces. Two user tests, one using composite creators and the other external evaluators, show that the new method produces higher rated composites that are better recognised

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References


Davies, G., van der Willik, P., & Morrison, L. J. (2000). Facial composite production: A comparison of mechanical and computer-driven systems. Journal of Applied Psychology, 85(1), 119. https://doi.org/10.1037/0021-9010.85.1.119

Frowd, C. D., McQuiston-Surrett, D., Anandaciva, S., Ireland, C. G., & Hancock, P. J. (2007). An evaluation of US systems for facial composite production. Ergonomics, 50(12), 1987-1998. https://doi.org/10.1080/00140130701523611

Hancock, P. J. (2000). Evolving faces from principal components. Behavior Research Methods, Instruments, & Computers, 32(2), 327-333. https://doi.org/10.3758/bf03207802

Solomon, C. J., Gibson, S. J., & Mist, J. J. (2013). Interactive evolutionary generation of facial composites for locating suspects in criminal investigations. Applied Soft Computing, 13(7), 3298-3306. https://doi.org/10.1016/j.asoc.2013.02.010

Tredoux, C., Nunez, D., Oxtoby, O., & Prag, B. (2006). An evaluation of ID: An eigenface based construction system. South African Computer Journal, 37, 90-97.

Kurt, B., Etaner-Uyar, A. S., Akbal, T., Demir, N., Kanlikilicer, A. E., Kus, M. C., & Ulu, F. H. (2006). Active appearance model-based facial composite generation with interactive natureinspired heuristics. International Workshop on Multimedia Content Representation, Classification and Security, 2006. pp. 183-190. https://doi.org/10.1007/11848035_26

Frowd, C. D., Hancock, P. J., & Carson, D. (2004). EvoFIT: A holistic, evolutionary facial imaging technique for creating composites. ACM Transactions on Applied Perception, 1(1), 19-39. https://doi.org/10.1145/1008722.1008725

Frowd, C. D., Pitchford, M., Bruce, V., Jackson, S., Hepton, G., Greenall, M., ... & Hancock, P. J. (2011). The psychology of face construction: Giving evolution a helping hand. Applied Cognitive Psychology, 25(2), 195-203. https://doi.org/10.1002/acp.1662

Frowd, C. D., Skelton, F., Atherton, C., Pitchford, M., Hepton, G., Holden, L., ... & Hancock, P. J. (2012). Recovering faces from memory: the distracting influence of external facial features. Journal of Experimental Psychology: Applied, 18(2), 224. https://doi.org/10.1037/a0027393

Frowd, C. D., Pitchford, M., Skelton, F., Petkovic, A., Prosser, C., & Coates, B. (2012). Catching even more offenders with EvoFIT facial composites. IEEE Third International Conference on Emerging Security Technologies (EST), 2012. pp. 20-26 https://doi.org/10.1109/est.2012.26

Ellis, H. D., Shepherd, J. W., & Davies, G. M. (1979). Identification of familiar and unfamiliar faces from internal and external features: Some implications for theories of face recognition. Perception, 8(4), 431-439. https://doi.org/10.1068/p080431

Frowd, C., Bruce, V., McIntyre, A., & Hancock, P. (2007). The relative importance of external and internal features of facial composites. British Journal of Psychology, 98(1), 61-77. https://doi.org/10.1348/000712606x104481

Frowd, C., Park, J., McIntyre, A., Bruce, V., Pitchford, M., Fields, S., Kenirons, M. & Hancock, P. J. (2008). Effecting an improvement to the fitness function. How to evolve a more identifiable face. IEEE ECSIS Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS'08), 2008. pp. 3-10. https://doi.org/10.1109/bliss.2008.28

Hancock, P. J., Bruce, V., & Burton, A. M. (2000). Recognition of unfamiliar faces. Trends in Cognitive Sciences, 4(9), 330-337. https://doi.org/10.1016/s1364-6613(00)01519-9

Tanaka, J. W., & Sengco, J. A. (1997). Features and their configuration in face recognition. Memory & Cognition, 25(5), 583-592. https://doi.org/10.3758/bf03211301

Frowd, C. D., Bruce, V., Plenderleith, Y., & Hancock, P. J. B. (2006). Improving target identification using pairs of composite faces constructed by the same person. IET Conference on Crime and Security, 2006. pp. 390-395. https://doi.org/10.1049/ic:20060341

Little, A. C., & Hancock, P. J. (2002). The role of masculinity and distinctiveness in judgments of human male facial attractiveness. British Journal of Psychology, 93(4), 451-464. https://doi.org/10.1348/000712602761381349

Kemp, R., Pike, G., White, P., & Musselman, A. (1996). Perception and recognition of normal and negative faces: The role of shape from shading and pigmentation cues. Perception, 25(1), 37-52. https://doi.org/10.1068/p250037

Yip, A. W., & Sinha, P. (2002). Contribution of color to face recognition. Perception, 31(8), 995- 1003. https://doi.org/10.1068/p3376

Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.

Craw, I., & Cameron, P. (1991). Parameterising images for recognition and reconstruction. British Machine Vision Conference. Springer London. pp. 367-370. https://doi.org/10.5244/c.5.52

Hancock, P. J., Burton, A. M., & Bruce, V. (1996). Face processing: Human perception and principal components analysis. Memory & Cognition, 24(1), 26-40. https://doi.org/10.3758/bf03197270

Bruce, V., Hanna, E., Dench, N., Healey, P., & Burton, M. (1992). The importance of ‘mass’ in line drawings of faces. Applied Cognitive Psychology, 6(7), 619-628. https://doi.org/10.1002/acp.2350060705

O'Toole, A. J., Vetter, T., Blanz, V. (1999) Threedimensional shape and two-dimensional surface reflectance contributions to face recognition: An application of three-dimensional morphing. Vision Research, 39, 3145-3155. https://doi.org/10.1016/s0042-6989(99)00034-6

Sinha, P., Balas, B. J., Ostrovsky, Y. & Russell, R. (2006). Face recognition by humans. In Zhao, W. and Chellappa, R. (Eds.), Face processing: Advanced modeling and methods, Amsterdam: Elsevier/Academic Press, 257-292

Shah, M. (1997). Fundamentals of computer vision (Unpublished manuscript). University of Central Florida.

Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 679-698. https://doi.org/10.1109/tpami.1986.4767851

Pérez, P., Gangnet, M., & Blake, A. (2003). Poisson image editing. ACM Transactions on Graphics, 22(3), 313-318. https://doi.org/10.1145/882262.882269

Garcia-Zurdo, R. (2016). Evolutive gradient face compositing using the Poisson equation. Perception, 45(2), 25-26.

Burton, A. M., White, D., & McNeill, A. (2010). The Glasgow face matching test. Behavior Research Methods, 42(1), 286-291. https://doi.org/10.3758/brm.42.1.286

Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. IEEE Conference on Computer Vision and Pattern Recognition, 2014. pp. 1867-1874. https://doi.org/10.1109/cvpr.2014.241

Liu, J., Mei, K., Ge, C., & Zheng, N. (2011). Interactive Poisson photometric propagation for facial composite. 1st International Symposium on Access Spaces (ISAS), 2011, pp. 121-126. https://doi.org/10.1109/isas.2011.5960932

Riviere, M., Teytaud, O., Rapin, J., LeCun, Y. and Couprie, C. (2019). Inspirational adversarial image generation. arXiv:1906.11661.

Appendix. Gradient integration by solving Poisson’s equation




DOI: https://doi.org/10.31449/inf.v44i1.2340

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