Theoretical Difficulties of Automatic Face RecognitionExtensive research has been devoted to the development of computer algorithms, advances in automatic face recognition have led to the development of systems that operate at high performance in a environment controlled setting. Although computerized AFR systems do not necessarily mimic human face recognition processes, humans often compete with the ability of computer algorithms when it comes to recognizing familiar faces even under highly degraded conditions. Therefore, attempting to understand how humans process familiar and unfamiliar faces may provide insight into why machines struggle in some cases and perhaps how they can be improved. Most computer systems strive for high-resolution detail in an attempt to be able to discriminate between the finest nuances. differences in individual characteristics. However, even among the most advanced systems, sometimes the target image condition is uncontrollably low quality and the system fails, yet evidence suggests that human face recognition for familiar faces does not always succumb to this obstacle. Burton, Wilson, Cohen, and Bruce (1999) examined human face recognition for familiar and unfamiliar faces in poor-quality video images. Subjects were shown low-quality video sequences containing a face that the participant was either familiar with or unfamiliar with. They were then presented with high resolution photos and asked to determine if people appeared in the clips, all groups scored on the seen targets significantly higher than the unseen targets, however performance was significantly more accurate for the familiar group . In a subsequent experiment, Burton et al. (1999) further studied the basis of familiar face recognition,......at the heart of the paper...extraordinary face recognition ability. Psychonomic Bulletin and Review, 16, 252-257. Searcy, J. H., & Bartlett, J. C. (1996). Inversion and processing of component and spatio-relational information in faces. Journal of Experimental Psychology: Human, Perception and Performance, 22, 904-915.Stephan, B. C. M., & Caine, D. (2007). What's in a view? The role of feature information in the recognition of unfamiliar faces through viewpoint transformation. Perception, 36, 189-198. Tanka, J. W., & Farah, M. J. (1993). Parts and wholes in face recognition. The Quarterly Journal of Experimental Psychology, 46, 225-245. Tolba, A. S., El-Baz, A. H., & El-Harby, A. A. (2006). Facial recognition: a literature review. World Academy of Science, Engineering and Technology, 19, 319-334.Yip, A. W., & Sinha, P. (2002). Contribution of color to face recognition. Perception, 31, 995-1003.