Computer Image Retrieval by Features: Selecting the Best Facial Features for Suspect Identification Systems.

Eric S. Lee, Thomas Whalen: Computer Image Retrieval by Features: Selecting the Best Facial Features for Suspect Identification Systems. CIKM 1994: 105-111

@inproceedings{DBLP:conf/cikm/LeeW94,
  author    = {Eric S. Lee and
               Thomas Whalen},
  title     = {Computer Image Retrieval by Features: Selecting the Best Facial
               Features for Suspect Identification Systems},
  booktitle = {Proceedings of the Third International Conference on Information
               and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November
               29 - December 2, 1994},
  publisher = {ACM},
  year      = {1994},
  pages     = {105-111},
  ee        = {db/conf/cikm/LeeW94.html, http://doi.acm.org/10.1145/191246.191266},
  crossref  = {DBLP:conf/cikm/94},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Correct suspect identification of known offenders by witnesses deteriorates rapidly as more are examined in mugshot albums. Feature approaches, where mugshots are displayed in order of similarity to witnesses' descriptions, increase identification success by reducing this number. System performance depends on selection of system features. Four methods of selecting features are evaluated empirically: theory, random, hill-climbing algorithm, and hybrid. The theory asserts success depends on five properties of system features: informativeness, orthogonality, sufficiency, consistency, and observability. Comparing system performance on the best 10 features selected (from a pool of 90) by each method supports our contention. In four experimental tests of a system with 1000 official mugshots, over 90% of witness searches resulted in photos of target suspects retrieved in the first ten mugshots displayed for examination (using all 90 system features). On average, suspects were retrieved in the first 54, 7, 22, and 70 mugshots when using only the best 10 model features. Hybrid and hill-climbing algorithms did not improve on this performance, and performance of randomly selected sets of 10 features was poor.

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Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November 29 - December 2, 1994. ACM 1994
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