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Essay / Research Paper Abstract
This 4 page paper discusses face recognition, both in humans and in automatic systems. Bibliography lists 22 sources.
Page Count:
4 pages (~225 words per page)
File: D0_HVfcerec.rtf
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Unformatted sample text from the term paper:
review. Because of space limitations, each review is extremely brief. At the end of the list a conclusion describes general trends found in the literature, if any. The articles are
in no particular order. Eimer writes that evidence from different sources indicates that the process of face identification is qualitatively different from identification of "non-face objects" (2000, p. 694).
He notes that recognition of non-face objects is based primarily on recognition of the constituent parts, whereas facial recognition "depends on holistic representations" (Eimer, 2000, p. 694). Nieuwenhuis and her
colleagues explore the way in which "repetitions, time and sleep" cause "profound changes" in long-term "memory representations" (Nieuwenhuis et al, 2008, p. 1913). They conclude that the visual system performs
more tasks than simply visual perception and that the reorganization in memory caused by repetition helps to stabilize them (Nieuwenhuis et al, 2008). Grudin writes that face recognition is difficult
for automated systems because of "large variations in facial appearance, head size and orientation, and changes in environmental conditions" (Grudin, 2000, p. 1161). His paper reviews developments in the field.
Sim et al describe the real-world tasks that must be performed by a facial recognition system, and the technology required to achieve them (2000). Pazo-Alvareza et al discuss the
fact that people generally associate faces and names and examines the "brain activity patterns" that take place during this process of "cross-modal encoding of names and faces" (Pazo-Alvareza et al,
2008, p. 192). Huang and his colleagues describe a neural network that will be able to "recognize human faces with any view in a certain viewing angle range (from left
30 degrees to right 30 degrees out of plane rotation)" (Huang et al, 2000). They note that their network is more accurate than conventional models because they are using "view-specific
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