Fusion of facial regions using color information in a forensic scenario

Comunicación presentada en: 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013; Havana; Cuba; 20-23 November 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41827-3_50This paper reports an analysis of the benefits of using color information on a region-based face recognition system. Three different color spaces are analysed (RGB, YCbCr, lαβ) in a very challenging scenario matching good quality mugshot images against video surveillance images. This scenario is of special interest for forensics, where examiners carry out a comparison of two face images using the global information of the faces, but paying special attention to each individual facial region (eyes, nose, mouth, etc.). This work analyses the discriminative power of 15 facial regions comparing both the grayscale and color information. Results show a significant improvement of performance when fusing several regions of the face compared to just using the whole face image. A further improvement of performance is achieved when color information is consideredThis work has been partially supported by contract with Spanish Guardia Civil and projects BBfor2 (FP7-ITN-238803), bio-Challenge (TEC2009-11186), Bio Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica

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

Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

Identification using face regions: Application and assessment in forensic scenarios

This is the author’s version of a work that was accepted for publication in Forensic Science International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International, 23, 1-3, (2013) DOI: 10.1016/j.forsciint.2013.08.020This paper reports an exhaustive analysis of the discriminative power of the different regions of the human face on various forensic scenarios. In practice, when forensic examiners compare two face images, they focus their attention not only on the overall similarity of the two faces. They carry out an exhaustive morphological comparison region by region (e.g., nose, mouth, eyebrows, etc.). In this scenario it is very important to know based on scientific methods to what extent each facial region can help in identifying a person. This knowledge obtained using quantitative and statical methods on given populations can then be used by the examiner to support or tune his observations. In order to generate such scientific knowledge useful for the expert, several methodologies are compared, such as manual and automatic facial landmarks extraction, different facial regions extractors, and various distances between the subject and the acquisition camera. Also, three scenarios of interest for forensics are considered comparing mugshot and Closed-Circuit TeleVision (CCTV) face images using MORPH and SCface databases. One of the findings is that depending of the acquisition distances, the discriminative power of the facial regions change, having in some cases better performance than the full face

On Using Gait in Forensic Biometrics

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

Applying psychological science to the CCTV review process: a review of cognitive and ergonomic literature

As CCTV cameras are used more and more often to increase security in communities, police are spending a larger proportion of their resources, including time, in processing CCTV images when investigating crimes that have occurred (Levesley & Martin, 2005; Nichols, 2001). As with all tasks, there are ways to approach this task that will facilitate performance and other approaches that will degrade performance, either by increasing errors or by unnecessarily prolonging the process. A clearer understanding of psychological factors influencing the effectiveness of footage review will facilitate future training in best practice with respect to the review of CCTV footage. The goal of this report is to provide such understanding by reviewing research on footage review, research on related tasks that require similar skills, and experimental laboratory research about the cognitive skills underpinning the task. The report is organised to address five challenges to effectiveness of CCTV review: the effects of the degraded nature of CCTV footage, distractions and interrupts, the length of the task, inappropriate mindset, and variability in people’s abilities and experience. Recommendations for optimising CCTV footage review include (1) doing a cognitive task analysis to increase understanding of the ways in which performance might be limited, (2) exploiting technology advances to maximise the perceptual quality of the footage (3) training people to improve the flexibility of their mindset as they perceive and interpret the images seen, (4) monitoring performance either on an ongoing basis, by using psychophysiological measures of alertness, or periodically, by testing screeners’ ability to find evidence in footage developed for such testing, and (5) evaluating the relevance of possible selection tests to screen effective from ineffective screener

Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters

In this investigation we identify relationships between human and automated face recognition systems with respect to compression. Further, we identify the most influential scene parameters on the performance of each recognition system. The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance, facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the human visibility of useful facial information, but not much work has been carried out to assess the influence they have on automated recognition systems. In this investigation, the methodology previously employed in the human investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis, Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging systems for security applications

The detection of concealed firearm carrying trough CCTV: the role of affect recognition

This research aimed to explore whether the recognition of offenders with a concealed firearm by a human operator might be based on the recognition of affective (negative) state derived from non-verbal behaviour that is accessible from CCTV images. Since a firearm is concealed, it has been assumed that human observers would respond to subtle cues which individuals inherently produce whilst carrying a hidden firearm. These cues are believed to be reflected in the body language of those carrying firearms and might be apprehended by observers at a conscious or subconscious level. Another hypothesis is that the ability to recognize the carrier of concealed firearm in the CCTV footage might be affected by other factors, such as the skills in decoding an affective state of others and the viewpoint of observation of the surveillance targets. In order to give a theoretical and experimental basis for these hypotheses the first objective was to examine the extant literature to determine what is known about recognition of affect from non-verbal cues (e.g. facial expressions and body movement), and how it can be applied to the detection of human mal-intent. A second objective was to explore this subject in relation to the detection of concealed firearm carrying through performing a number of experimental studies. The studies employed experts, i.e. CCTV operators and mainly the lay people as participants. Also, various experimental techniques such as questionnaires and eye-tracking registration were used to investigate the topic. The results show that human observers seem to use visual indicators of affective state of surveillance targets to make a decision whether or not the individuals are carrying a concealed firearm. The most prominent cues were face, and upper body of surveillance targets, gait, posture and arm movements. The test of decoding ability did not show sufficient relationship with the ability to detect a concealed firearm bearer. The performance on the task might be view dependent. Further research into this topic will be needed to generate strategies that would support reliable detection of concealed firearm carrying through employing of related affective behavioural cues

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information