The effect of image pixelation on unfamiliar face matching

Low-resolution, pixelated images from CCTV can be used to compare the perpetrators of crime with high-resolution photographs of potential suspects. The current study investigated the accuracy of person identification under these conditions, by comparing high-resolution and pixelated photographs of unfamiliar faces in a series of matching tasks. Performance decreased gradually with different levels of pixelation and was close to chance with a horizontal image resolution of only 8 pixel bands per face (Experiment 1). Matching accuracy could be improved by reducing the size of pixelated faces (Experiment 2) or by varying the size of the to-be-compared-with high-resolution face image (Experiment 3). In addition, pixelation produced effects that appear to be separable from other factors that might affect matching performance, such as changes in face view (Experiment 4). These findings reaffirm that criminal identifications from CCTV must be treated with caution and provide some basic estimates for identification accuracy with different pixelation levels. This study also highlights potential methods for improving performance in this task

Viewers base estimates of face matching accuracy on their own familiarity: Explaining the photo-ID paradox

Matching two different images of a face is a very easy task for familiar viewers, but much harder for unfamiliar viewers. Despite this, use of photo-ID is widespread, and people appear not to know how unreliable it is. We present a series of experiments investigating bias both when performing a matching task and when predicting other people’s performance. Participants saw pairs of faces and were asked to make a same/different judgement, after which they were asked to predict how well other people, unfamiliar with these faces, would perform. In four experiments we show different groups of participants familiar and unfamiliar faces, manipulating this in different ways: celebrities in experiments 1 to 3 and personally familiar faces in experiment 4. The results consistently show that people match images of familiar faces more accurately than unfamiliar faces. However, people also reliably predict that the faces they themselves know will be more accurately matched by different viewers. This bias is discussed in the context of current theoretical debates about face recognition, and we suggest that it may underlie the continued use of photo-ID, despite the availability of evidence about its unreliability

Evidence for view-invariant Face Recognition Units in unfamiliar face learning

Many models of face recognition incorporate the idea of a face recognition unit (FRU). This is an abstracted representation formed from each experience of a face. Longmore et al. (2008) devised a face learning experiment to investigate such a construct (i.e., view-invariance) but failed to find evidence of its existence. Three experiments developed Longmore et al.’s study further by using a different learning task, by employing more stimuli. One or two views of previously unfamiliar faces were shown to participants in a serial matching task (learning). Later, participants attempted to recognise both seen and novel views of the learned faces. Experiment one tested participants’ recognition of a novel view, a day after learning. Experiment two was identical, but tested participants on the same day as learning. And experiment three repeated experiment one, but tested participants on a novel view that was outside the rotation of those views learned. Results revealed a significant advantage for recognising a novel view when two views had been learned, rather than a single learned view – for all experiments. The effect of view-invariance found when both views were learned is discussed

Development of an Ada package library

A usable prototype Ada package library was developed and is currently being evaluated for use in large software development efforts. The library system is comprised of an Ada-oriented design language used to facilitate the collection of reuse information, a relational data base to store reuse information, a set of reusable Ada components and tools, and a set of guidelines governing the system's use. The prototyping exercise is discussed and the lessons learned from it have led to the definition of a comprehensive tool set to facilitate software reuse