Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites

Facial composite construction is one of the most successful applications of interactive evolutionary computation. In spite of this, previous work in the area of composite construction has not investigated the algorithm design options in detail. We address this issue with four experiments. In the first experiment a sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second experiment the performances of two mutation and two recombination operators for interactive genetic algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive genetic algorithm to interactive differential evolution. Our results show that the facial composite construction process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human face perception and differences between the participants in how they interact with the algorithms. Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora. V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani 12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo. Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma. To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom

Portraits, Likenesses, Composites? Facial Difference in Forensic Art

The police composite sketch is arguably the most fundamental example of forensic art, and one which enjoys considerable cultural prominence. Intended to produce a positive identification of a specific individual, composites are a form of visual intelligence rather than hard evidence. Based on verbal descriptions drawn from memory deriving from highly contingent and possibly traumatic events, composites are by definition unique and precarious forensic objects, representing an epistemological paradox in their definition as simultaneous ‘artistic impression’ and ‘pictorial statement’. And despite decades of operational use, only in recent years has the field of cognitive psychology begun to fully understand and address the conditions that affect recognition rates both positively and negatively. How might composites contribute to our understanding of representational concepts such as ‘likeness’ and ‘accuracy’? And what role does visual medium – drawn, photographic or computerized depiction – play in the legibility of these images? Situated within the broader context of forensic art practices, this paper proceeds from an understanding that the face is simultaneously crafted as an analogy of the self and a forensic technology. In other words, the face is a space where concepts of identification and identity, sameness and difference (often uncomfortably) converge. With reference to selected examples from laboratory research, field application and artistic practice, I consider how composites, through their particular techniques and form, contribute to subject-making, and how they embody the fugitive, in literal and figurative terms

EvoFIT: A Holistic, Evolutionary Facial Imaging System

This thesis details the development and evaluation of a new photofitting approach. The motivation for this work is that current photofit systems used by the police - whether manual or computerized - do not appear to work very well. Part of the problem with these approaches is they involve a single facial representation that necessitates a verbal interaction. When a multiple presentation is considered, our innate ability to recognize faces is capitalized (and the potentially disruptive effect of the verbal component is reduced). The approach works by employing Genetic Algorithms to evolve a small group of faces to be more like a desired target. The main evolutionary influence is via user input that specifies the similarity of the presented images with the target under construction. The thesis follows three main phases of development. The first involves a simple system modelling the internal components of a face (eyes, eyebrows, nose and mouth) containing features in a fixed relationship with each other. The second phase applies external facial features (hair and ears) along with an appropriate head shape and changes in the relationship between features. That the underlying model is based on Principal Components Analysis captures the statistics of how faces vary in terms of shading, shape and the relationship between features. Modelling was carried out in this way to create more realistic looking photofits and to guard against implausible featural relationships possible with traditional approaches. The encouraging results of these two sections prompted the development of a full photofit system: EvoFIT. This software is shown to have continued promise both in the lab and in a real case. Future work is directed particularly at resolving issues concerning the anonymity of the database faces and the creation of photofits from the subject's memory of a target

Using genetic algorithms to uncover individual differences in how humans represent facial emotion

Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test-retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research

EigenFIT : a statistical learning approach to facial composites

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advances in Facial Composite Technology, Utilizing Holistic Construction, Do Not Lead to an Increase in Eyewitness Misidentifications Compared to Older Feature-Based Systems

An eyewitness can contribute to a police investigation both by creating a composite image of the face of the perpetrator and by attempting to identify them during an identification procedure. This raises the potential issue that creating a composite of a perpetrator might then interfere with the subsequent identification of that perpetrator. Previous research exploring this issue has tended to use older feature-based composite systems, but the introduction of new holistic composite systems is an important development as they were designed to be a better match for human cognition and are likely to interact with memory in a different way. This issue was explored in the current experiment. Participants were randomly assigned to a feature-based composite construction condition (using E-FIT), a holistic-based composite construction condition (using EFIT-V) or a control condition. An ecologically valid delay between seeing a staged crime, creating the composite, and completing the identification task was employed to better match conditions in real investigations. The results showed that neither type of composite construction had an effect on participants’ accuracy on a subsequent identification task. This suggests that facial composite systems, including holistic systems, may not negatively impact subsequent eyewitness identification evidence

Race and statistics in facial recognition: Producing types, physical attributes, and genealogies

Principal component analysis (PCA) is a common statistical procedure. In forensics, it is used in facial recognition technologies and composite sketching systems. PCA is especially helpful in contexts with high facial diversity, which is often translated as racial diversity. In these settings, researchers use PCA to define a ‘normal face’ and organize the rest of the available facial diversity based on their resemblance to or difference from that norm. In this way, the use of PCA introduces an ‘ontology of the normal’ in which expectations about how a normal face should look are corroborated by statistical calculations of normality. I argue that the use of PCA can lead to a statistical reification of racial stereotypes that informs recognition practices. I discuss current and historical cases in which PCA is used: one of face perception theorization (‘face space theory’) and two of technology development (the ‘eigenfaces’ facial recognition algorithm and the ‘EvoFIT’ composite sketching system). In each, PCA aligns facial normality with racial expectations, and instrumentalizes race in specific ways: as a type, physical attribute, or genealogy. This analysis of PCA does two things. First, it opens the black box of facial recognition to uncover how stereotypes and intuitions about normality become part of theories and technologies of facial recognition. Second, it explains why racial categorizations remain central in contemporary identification technologies and other forensic practices

Varieties of biometric facial techniques for detecting offenders

Many crimes are committed where the only record of the event is in the memory of a witness or victim. Recovering a recognisable image of the offender’s face is then crucial for solving the crime. Traditionally, eyewitnesses describe the offender’s face and select individual facial features – eyes, hair, nose, etc. – to build a ‘composite’. This image is then published in the media so that someone can recognise it and phone the police with a name. Unfortunately, when tested using life-like procedures, this method rarely produces recognisable images. The current paper describes these systems for extracting such biometric information from witnesses. It also describes how useful they are and explores three such approaches for improving their effectiveness. Included are a new method to interview witnesses (a holistic-cognitive interview), a new method to present images to the public (animated composite) and a new system to construct the face (EvoFIT)

Evolving the Face of a Criminal: How to Search a Face Space More Effectively

Witnesses and victims of serious crime are often required to construct a facial composite, a visual likeness of a suspect’s face. The traditional method is for them to select individual facial features to build a face, but often these images are of poor quality. We have developed a new method whereby witnesses repeatedly select instances from an array of complete faces and a composite is evolved over time by searching a face model built using PCA. While past research suggests that the new approach is superior, performance is far from ideal. In the current research, face models are built which match a witness’s description of a target. It is found that such ‘tailored’ models promote better quality composites, presumably due to a more effective search, and also that smaller models may be even better. The work has implications for researchers who are using statistical modelling techniques for recognising faces

Improving target identification using pairs of composite faces constructed by the same person

Facial composites produced using traditional feature-based systems are notoriously hard to recognise. We have been developing a new more recognition-based system called EvoFIT that is performing better than other computerised approaches. In the current work, potential ways of improving performance even further were explored. It was found that asking the same person to construct two composites of a target face was successful in improving target identification. The data also found that composites constructed second were as identifiable as those constructed first, suggesting that the system does not appear to be interfering with a user’s memory of a target face. The work also indicated that switching from a monochrome to a colour face model produced a slight decrement in performance. Lastly, the work replicated a previous finding that constructing a composite of a distinctive face produces a more identifiable rendition than a composite of a more average-looking face