Name
Abstract | ||
|---|---|---|
Eyewitness identification remains an important element in judicial proceedings. It is very convincing, yet it is not very accurate. To better understand eyewitness identification, we began by examining how people understand similarity. This paper reports on analysis of study that examined how people made similarity judgements amongst a variety of facial photographs: participants were presented with a randomly ordered set of photos, with equal numbers of Caucasian (C) and First Nations (F), which they sorted based on their individual assessment of similarity. The number of piles made by the participants was not restricted. After sorting was complete, each participant was then asked to label each pile with a description of the pile's contents. Following the results of an earlier study, we hypothesize that individuals may be using different strategies to assess similarity between photos. In this analysis, we attempt to use the descriptive pile labels (in particular, related to lips and ears) as a means to uncover differences in strategies for which a classifier can be built, using the rough set attribute reduction methodology. In particular, we aim to identify those pairs of photographs that may be the key for verifying an individual's abilities and strategies when recognizing faces. The paper describes the method for data processing that enabled the comparisons based on labels. Continued success with the same technique as previously reported to filter pairs before performing the rough sets analysis, lends credibility to its use as a general method. The rough set techniques enable the identification of the sets of photograph pairs that are key to the divisions based on various strategies. This may lead to a practical test for people's abilities, as well as to inferring what discriminations people use in face recognition. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13529-3_61 | RSCTC |
Keywords | Field | DocType |
rough sets analysis,similarity judgement,rough set technique,rough set attribute reduction,descriptive pile label,eyewitness identification,individual assessment,paper report,facial photograph,general method,verbal description,earlier study,face recognition,data processing,rough set | Data mining,Facial recognition system,Data processing,Decision table,Credibility,Eyewitness identification,Computer science,Rough set,Sorting,Natural language processing,Artificial intelligence,Classifier (linguistics) | Conference |
Volume | ISSN | ISBN |
6086 | 0302-9743 | 3-642-13528-5 |
Citations | PageRank | References |
2 | 0.42 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daryl H. Hepting | 1 | 80 | 15.55 |
| Richard Spring | 2 | 10 | 1.74 |
| Timothy Maciag | 3 | 10 | 2.49 |
| Katherine Arbuthnott | 4 | 7 | 0.95 |
| Dominik Ślęzak | 5 | 553 | 50.04 |