Computerized feature systems for identifying suspects

In suspect identification, witnesses examine photos of known offenders in mugshot albums. The probability of correct identification deteriorates rapidly, however, as the number of mugshots examined increases. Feature approaches, where mugshots are displayed in order of similarity to witness descriptions of suspects, increase identification success by reducing this number. In our computerized feature system, both police raters and witnesses describe facial features of suspects on rating scales such as nose size: small 1 2 3 4 5 large. Feature users consistently identify more target suspects correctly than do album users. Previous experimental tests have failed, however, to examine the effects of feature system performance of the use of live targets as suspects rather than photos, the use of realistic crime scenarios, the number of police raters/mugshot, and differences among raters in their effect on system perfomance. In three experiments, we investigated those four issues. The first experiment used photos as target suspects but with multiple distractors, the second tested live suspects, while the third tested live suspects in a realistic crime scenario. The database contained the official mugshots of 1,000 offenders. Across the three experiments, a second and sometimes a third rater/mugshot significantly reduced the number of photos examined. More raters/mugshot did not affect performance further. Raters differed significantly in their effect on system perfomance. Significantly, our feature system performed well both with target suspects seen live and with live suspects in realistic crime scenarios (performance was comparable to that in previous experiments for photos of target suspects). These results strongly support our contention that feature systems are superior to album systems.