Fact or Friction: examination of the transparency, reliability and sufficiency of the ACE-V method of fingerprint analysis

Three studies are presented which provide a mixed methods exploration of fingerprint analysis. Using a qualitative approach (Expt 1), expert analysts used a ‘think aloud’ task to describe their process of analysis. Thematic analysis indicated consistency of practice, and experts’ comments underpinned the development of a training tool for subsequent use. Following this, a quantitative approach (Expt 2) assessed expert reliability on a fingerprint matching task. The results suggested that performance was high and often at ceiling, regardless of the length of experience held by the expert. As a final test, the experts’ fingerprint analysis method was taught to a set of naïve students, and their performance on the fingerprint matching task was compared both to the expert group and to an untrained novice group (Expt 3). Results confirmed that the trained students performed significantly better than the untrained students. However, performance remained substantially below that of the experts. Several explanations are explored to account for the performance gap between experts and trained novices, and their implications are discussed in terms of the future of fingerprint evidence in court

Combining Forces: Data fusion across man and machine for biometric analysis

Through the HUMMINGBIRD framework outlined here,we seek to encourage a novel multidisciplinary approach to biometric analysis with the goal of enhancing both understanding and accuracy of identification

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications

Towards automated eyewitness descriptions: describing the face, body and clothing for recognition

A fusion approach to person recognition is presented here outlining the automated recognition of targets from human descriptions of face, body and clothing. Three novel results are highlighted. First, the present work stresses the value of comparative descriptions (he is taller than…) over categorical descriptions (he is tall). Second, it stresses the primacy of the face over body and clothing cues for recognition. Third, the present work unequivocally demonstrates the benefit gained through the combination of cues: recognition from face, body and clothing taken together far outstrips recognition from any of the cues in isolation. Moreover, recognition from body and clothing taken together nearly equals the recognition possible from the face alone. These results are discussed with reference to the intelligent fusion of information within police investigations. However, they also signal a potential new era in which automated descriptions could be provided without the need for human witnesses at all

Understanding person acquisition using an interactive activation and competition network

Face perception is one of the most developed visual skills that humans display, and recent work has attempted to examine the mechanisms involved in face perception through noting how neural networks achieve the same performance. The purpose of the present paper is to extend this approach to look not just at human face recognition, but also at human face acquisition. Experiment 1 presents empirical data to describe the acquisition over time of appropriate representations for newly encountered faces. These results are compared with those of Simulation 1, in which a modified IAC network capable of modelling the acquisition process is generated. Experiment 2 and Simulation 2 explore the mechanisms of learning further, and it is demonstrated that the acquisition of a set of associated new facts is easier than the acquisition of individual facts in isolation of one another. This is explained in terms of the advantage gained from additional inputs and mutual reinforcement of developing links within an interactive neural network system. <br/

Familiarity is key : exploring the effect of familiarity on the face-voice correlation

Recent research has examined the extent to which face and voice processing are associated by virtue of the fact that both tap into a common person perception system. However, existing findings do not yet fully clarify the role of familiarity in this association. Given this, two experiments are presented that examine face-voice correlations for unfamiliar stimuli (Experiment 1) and for familiar stimuli (Experiment 2). With care being taken to use tasks that avoid floor and ceiling effects and that use realistic speech-based voice clips, the results suggested a significant positive but small-sized correlation between face and voice processing when recognizing unfamiliar individuals. In contrast, the correlation when matching familiar individuals was significant and positive, but much larger. The results supported the existing literature suggesting that face and voice processing are aligned as constituents of an overarching person perception system. However, the difference in magnitude of their association here reinforced the view that familiar and unfamiliar stimuli are processed in different ways. This likely reflects the importance of a pre-existing mental representation and cross-talk within the neural architectures when processing familiar faces and voices, and yet the reliance on more superficial stimulus-based and modality-specific analysis when processing unfamiliar faces and voices

Who am I? : Representing the self offline and in different online contexts

The present paper examines the extent to which self-presentation may be affected by the context in which is it undertaken. Individuals were asked to complete the Twenty Statements Test both privately and publicly, but were given an opportunity to withhold any of their personal information before it was made public. Four contexts were examined: an offline context (face-to-face), an un-contextualized general online context, or two specific online contexts (dating or job-seeking). The results suggested that participants were willing to disclose substantially less personal information online than offline. Moreover, disclosure decreased as the online context became more specific, and those in the job-seeking context disclosed the least amount of information. Surprisingly, individual differences in personality did not predict disclosure behavior. Instead, the results are set in the context of audience visibility and social norms, and implications for self-presentation in digital contexts are discussed

Predicting sex as a soft-biometrics from device interaction swipe gestures

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices