Paper
2 March 1994 Automatic classification of police mugshot album using principal component analysis
Noam Jungmann, Avraham Levi, Arie Aperman, Shimon Edelman
Author Affiliations +
Abstract
The principal components of a collection of randomly sampled photos from a police mugshot album were extracted using a modification of the neural net described by Sanger. Principal component analysis provides a basis that `spans' the face space, from which each face in a population can be reconstructed simply by taking a proper linear combination of the basis components. The coefficients of this linear combination can serve as a measure of similarity between faces. In previous studies, the authors tried a mugshot album search strategy based on subjective similarity judgments. A network of global, subjective similarity judgments was established between each photo in a small data base (3000 photos). The witness chose the photos most similar to the target from the set displayed on the monitor. The computer used the similarity network to rerank the remaining photos in the album that had not yet been displayed, to select the next set of photos with the best fit for presentation. This process was continued until the target photo was located. In this study, we used the objective similarity measure based on principal component coefficients in place of the subjective judgments. Each image in the experimental database was automatically coded using the first 100 principal components. The same experimental procedure as used with the manually coded data base was conducted. The results are better than those achieved with the subjective method and encourage the use of this coding scheme on larger police albums (100,000 photos).
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Noam Jungmann, Avraham Levi, Arie Aperman, and Shimon Edelman "Automatic classification of police mugshot album using principal component analysis", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170008
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KEYWORDS
Principal component analysis

Databases

Detection and tracking algorithms

Neural networks

Computing systems

Mouth

Applied mathematics

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