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Proceedings Paper

Using words as lexical basis functions for automatically indexing face images in a manner that correlates with human perception of similarity
Author(s): Mariano Phielipp; John A. Black Jr.; Sethuraman Panchanathan
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Paper Abstract

To facilitate collaboration between computers and people, computers should be able to perceive the world in a manner that correlates well with human perception. A good example of this is face image retrieval. Mathematically-based face indexing methods that are not based primarily on how humans perceive faces can produce retrievals that are disappointing to human users. This raises the question "Can human faces be automatically indexed in a manner that correlates well with human perception of similarity?" Humans use words to describe faces - words such as braided, graybearded, bearded, bespectacled, bald, blondish, blond, freckled, blue eyed, mustached, pale, Caucasian, brown eyed, dark skinned, or black eyed. Such words represent dimensions that span a shared concept space for faces. Therefore they might provide a useful guide to indexing faces in an intuitive manner. This paper describes research that uses descriptive words such as these to index faces. Each word guides the design of one feature detector that produces a scalar coefficient, and those coefficients collectively define a feature vector for each face. Given these feature vectors, it is possible to compute a similarity measure between pairs of faces, and to compare that computed similarity to the similarity, as perceived by humans.

Paper Details

Date Published: 9 February 2006
PDF: 12 pages
Proc. SPIE 6057, Human Vision and Electronic Imaging XI, 60571C (9 February 2006); doi: 10.1117/12.643692
Show Author Affiliations
Mariano Phielipp, Arizona State Univ. (United States)
John A. Black Jr., Arizona State Univ. (United States)
Sethuraman Panchanathan, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 6057:
Human Vision and Electronic Imaging XI
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

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