A multi-factor knuckle and nail bed verification tool for forensic imagery analysis.

Background: The grooming process involves sexually explicit images or videos sent by the offender to the minor. Although offenders may try to conceal their identity, these sexts often include hand, knuckle, and nail bed imagery.

Objective: We present a novel biometric hand verification tool designed to identify online child sexual exploitation offenders from images or videos based on biometric/forensic features extracted from hand regions.

Correction: Biometrics: Accessibility challenge or opportunity?

[This corrects the article DOI: 10.1371/journal.pone.

Biometrics: Accessibility challenge or opportunity?

Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease-of-use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario.

Exploring the relationship between stride, stature and hand size for forensic assessment.

Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system.

The importance of search strategy for finding targets in open terrain.

A number of real-world search tasks (i.e. police search, detection of improvised explosive devices (IEDs)) require searchers to search exhaustively across open ground.

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.

A feature based comparison of pen and swipe based signature characteristics.

Dynamic Signature Verification (DSV) is a biometric modality that identifies anatomical and behavioral characteristics when an individual signs their name. Conventionally signature data has been captured using pen/tablet apparatus. However, the use of other devices such as the touch-screen tablets has expanded in recent years affording the possibility of assessing biometric interaction on this new technology.

A learning model for the automated assessment of hand-drawn images for visuo-spatial neglect rehabilitation.

Visuo-spatial neglect (often simply referred to as "neglect") is a complex poststroke medical syndrome which may be assessed by means of a series of drawing-based tests. Based on a novel analysis of a test battery formed from established pencil-and-paper tests, the aim of this study is to develop an automated assessment system which enables objectivity, repeatability, and diagnostic capability in the scoring process. Furthermore, the novel assessment system encapsulates temporal sequence and other "dynamic" information inherent in the drawing process.

A new method for the synthesis of signature data with natural variability.

The collection of human biometric test data for system development and evaluation within any chosen modality generally requires significant time and effort if data are to be obtained in workable quantities. To overcome this problem, techniques to generate synthetic data have been developed. This paper describes a novel technique for the automatic synthesis of human handwritten-signature images, which introduces modeled variability within the generated output based on positional variation that is naturally found within genuine source data.

Using image analysis techniques to analyze figure-copying performance of patients with visuospatial neglect and control groups.

Figure copying is often used to detect visuospatial neglect (VSN) in brain-damaged patients. We describe algorithms that enable the computation of parameters for describing figure-copying performance. The researcher can readily implement these algorithms on a computer using image analysis software, and they provide information on goodness-of-fit, relative to a standard model, as well as on dynamic aspects of subjects' performance in completing figure copies.