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 [1]. 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 judgements. 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 re-rank 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).
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