A framework for forensic face recognition based on recognition performance calibrated for the quality of image pairs

Recently, it has been shown that performance of a face recognition system depends on the quality of both face images participating in the recognition process: the reference and the test image. In the context of forensic face recognition, this observation has two implications: a) the quality of the trace (extracted from CCTV footage) constrains the performance achievable using a particular face recognition system; b) the quality of the suspect reference set (to which the trace is matched against) can be judiciously chosen to approach optimal recognition performance under such a constraint. Motivated by these recent findings, we propose a framework for forensic face recognition that is based on calibrating the recognition performance for the quality of pairs of images. The application of this framework to several mock-up forensic cases, created entirely from the MultiPIE dataset, shows that optimal recognition performance, under such a constraint, can be achieved by matching the quality (pose, illumination, and, imaging device) of the reference set to that of the trace. This improvement in recognition performance helps reduce the rate of misleading interpretation of the evidence

Can we ID from CCTV? Image quality in digital CCTV and face identification performance

CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality

Sequential Dimensionality Reduction for Extracting Localized Features

Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an additive combination of nonnegative basis elements. Nonnegative matrix underapproximation (NMU) is a closely related technique that has the advantage to identify features sequentially. In this paper, we propose a variant of NMU that is particularly well suited for image analysis as it incorporates the spatial information, that is, it takes into account the fact that neighboring pixels are more likely to be contained in the same features, and favors the extraction of localized features by looking for sparse basis elements. We show that our new approach competes favorably with comparable state-of-the-art techniques on synthetic, facial and hyperspectral image data sets.Comment: 24 pages, 12 figures. New numerical experiments on synthetic data sets, discussion about the convergenc

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

Training methods for facial image comparison: a literature review

This literature review was commissioned to explore the psychological literature relating to facial image comparison with a particular emphasis on whether individuals can be trained to improve performance on this task. Surprisingly few studies have addressed this question directly. As a consequence, this review has been extended to cover training of face recognition and training of different kinds of perceptual comparisons where we are of the opinion that the methodologies or findings of such studies are informative. The majority of studies of face processing have examined face recognition, which relies heavily on memory. This may be memory for a face that was learned recently (e.g. minutes or hours previously) or for a face learned longer ago, perhaps after many exposures (e.g. friends, family members, celebrities). Successful face recognition, irrespective of the type of face, relies on the ability to retrieve the to-berecognised face from long-term memory. This memory is then compared to the physically present image to reach a recognition decision. In contrast, in face matching task two physical representations of a face (live, photographs, movies) are compared and so long-term memory is not involved. Because the comparison is between two present stimuli rather than between a present stimulus and a memory, one might expect that face matching, even if not an easy task, would be easier to do and easier to learn than face recognition. In support of this, there is evidence that judgment tasks where a presented stimulus must be judged by a remembered standard are generally more cognitively demanding than judgments that require comparing two presented stimuli Davies & Parasuraman, 1982; Parasuraman & Davies, 1977; Warm and Dember, 1998). Is there enough overlap between face recognition and matching that it is useful to look at the literature recognition? No study has directly compared face recognition and face matching, so we turn to research in which people decided whether two non-face stimuli were the same or different. In these studies, accuracy of comparison is not always better when the comparator is present than when it is remembered. Further, all perceptual factors that were found to affect comparisons of simultaneously presented objects also affected comparisons of successively presented objects in qualitatively the same way. Those studies involved judgments about colour (Newhall, Burnham & Clark, 1957; Romero, Hita & Del Barco, 1986), and shape (Larsen, McIlhagga & Bundesen, 1999; Lawson, Bülthoff & Dumbell, 2003; Quinlan, 1995). Although one must be cautious in generalising from studies of object processing to studies of face processing (see, e.g., section comparing face processing to object processing), from these kinds of studies there is no evidence to suggest that there are qualitative differences in the perceptual aspects of how recognition and matching are done. As a result, this review will include studies of face recognition skill as well as face matching skill. The distinction between face recognition involving memory and face matching not involving memory is clouded in many recognition studies which require observers to decide which of many presented faces matches a remembered face (e.g., eyewitness studies). And of course there are other forensic face-matching tasks that will require comparison to both presented and remembered comparators (e.g., deciding whether any person in a video showing a crowd is the target person). For this reason, too, we choose to include studies of face recognition as well as face matching in our revie

Understanding How Image Quality Affects Deep Neural Networks

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.Comment: Final version will appear in IEEE Xplore in the Proceedings of the Conference on the Quality of Multimedia Experience (QoMEX), June 6-8, 201

Perceptual impairment in face identification with poor sleep

Previous studies have shown impaired memory for faces following restricted sleep. However, it is not known whether lack of sleep impairs performance on face identification tasks that do not rely on recognition memory, despite these tasks being more prevalent in security and forensic professions—for example, in photo-ID checks at national borders. Here we tested whether poor sleep affects accuracy on a standard test of face-matching ability that does not place demands on memory: the Glasgow Face-Matching Task (GFMT). In Experiment 1, participants who reported sleep disturbance consistent with insomnia disorder show impaired accuracy on the GFMT when compared with participants reporting normal sleep behaviour. In Experiment 2, we then used a sleep diary method to compare GFMT accuracy in a control group to participants reporting poor sleep on three consecutive nights—and again found lower accuracy scores in the short sleep group. In both experiments, reduced face-matching accuracy in those with poorer sleep was not associated with lower confidence in their decisions, carrying implications for occupational settings where identification errors made with high confidence can have serious outcomes. These results suggest that sleep-related impairments in face memory reflect difficulties in perceptual encoding of identity, and point towards metacognitive impairment in face matching following poor sleep