An investigation of supervector regression for forensic voice comparison on small data

International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer

Strength of linguistic text evidence: A fused forensic text comparison system

Compared to other forensic comparative sciences, studies of the efficacy of the likelihood ratio (LR) framework in forensic authorship analysis are lagging. An experiment is described concerning the estimation of strength of linguistic text evidence within that framework. The LRs were estimated by trialling three different procedures: one is based on the multivariate kernel density (MVKD) formula, with each group of messages being modelled as a vector of authorship attribution features; the other two involve N-grams based on word tokens and characters, respectively. The LRs that were separately estimated from the three different procedures are logistic-regression-fused to obtain a single LR for each author comparison. This study used predatory chatlog messages sampled from 115 authors. To see how the number of word tokens affects the performance of a forensic text comparison (FTC) system, token numbers used for modelling each group of messages were progressively increased: 500, 1000, 1500 and 2500 tokens. The performance of the FTC system is assessed using the log-likelihood-ratio cost (Cllr), which is a gradient metric for the quality of LRs, and the strength of the derived LRs is charted as Tippett plots. It is demonstrated in this study that (i) out of the three procedures, the MVKD procedure with authorship attribution features performed best in terms of Cllr, and that (ii) the fused system outperformed all three of the single procedures. When the token length is 1500, for example, the fused system achieved a Cllr value of 0.15. Some unrealistically strong LRs were observed in the results. Reasons for these are discussed, and a possible solution to the problem, namely the empirical lower and upper bound LR (ELUB) method is trialled and applied to the LRs of the best-achieving fusion system

Forensic interpretation framework for body and gait analysis:feature extraction, frequency and distinctiveness

Surveillance is ubiquitous in modern society, allowing continuous monitoring of areas that results in capturing criminal (or suspicious) activity as footage. This type of trace is usually examined, assessed and evaluated by a forensic examiner to ultimately help the court make inferences about who was on the footage. The purpose of this study was to develop an analytical model that ensures applicability of morphometric (both anthropometric and morphological) techniques for photo-comparative analyses of body and gait of individuals in CCTV images, and then to assign a likelihood ratio. This is the first paper of a series: This paper will contain feature extraction to observe repeatability procedures from a single observer, in turn, producing the frequency and distinctiveness of the feature set within the given population. To achieve this, an Australian population database of 383 subjects (stance) and 268 subjects (gait) from both sexes, all ages above 18 and ancestries was generated. Features were extracted, defined, and their rarity viewed among the developed database. Repeatability studies were completed in which stance and gait (static and dynamic) features contained low levels of repeatability error (0.2%–1.5 TEM%). For morphological examination, finger flexion and feet placement were observed to have high observer performance.</p

Face comparison in forensics:A deep dive into deep learning and likelihood rations

This thesis explores the transformative potential of deep learning techniques in the field of forensic face recognition. It aims to address the pivotal question of how deep learning can advance this traditionally manual field, focusing on three key areas: forensic face comparison, face image quality assessment, and likelihood ratio estimation. Using a comparative analysis of open-source automated systems and forensic experts, the study finds that automated systems excel in identifying non-matches in low-quality images, but lag behind experts in high-quality settings. The thesis also investigates the role of calibration methods in estimating likelihood ratios, revealing that quality score-based and feature-based calibrations are more effective than naive methods. To enhance face image quality assessment, a multi-task explainable quality network is proposed that not only gauges image quality, but also identifies contributing factors. Additionally, a novel images-to-video recognition method is introduced to improve the estimation of likelihood ratios in surveillance settings. The study employs multiple datasets and software systems for its evaluations, aiming for a comprehensive analysis that can serve as a cornerstone for future research in forensic face recognition

Likelihood ratio calibration in a transparent and testable forensic speaker recognition framework

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. Ramos, J. González-Rodríguez, J. Ortega-garcía, "Likelihood Ratio Calibration in a Transparent and Testable Forensic Speaker Recognition Framework " in The Speaker and Language Recognition Workshop, ODYSSEY, San Juan (Puerto Rico), 2006, 1 - 8A recently reopened debate about the infallibility of some classical forensic disciplines is leading to new requirements in forensic science. Standardization of procedures, proficiency testing, transparency in the scientific evaluation of the evidence and testability of the system and protocols are emphasized in order to guarantee the scientific objectivity of the procedures. Those ideas will be exploited in this paper in order to walk towards an appropriate framework for the use of forensic speaker recognition in courts. Evidence is interpreted using the Bayesian approach for the analysis of the evidence, as a scientific and logical methodology, in a two-stage approach based in the similarity-typicality pair, which facilitates the transparency in the process. The concept of calibration as a way of reporting reliable and accurate opinions is also deeply addressed, presenting experimental results which illustrate its effects. The testability of the system is then accomplished by the use of the NIST SRE 2005 evaluation protocol. Recently proposed application-independent evaluation techniques (Cllr and APE curves) are finally addressed as a proper way for presenting results of proficiency testing in courts, as these evaluation metrics clearly show the influence of calibration errors in the accuracy of the inferential decision processThis work has been supported by the Spanish Ministry for Science and Technology under project TIC2003-09068-C02-01

Toward Fail-Safe Speaker Recognition: Trial-Based Calibration with a Reject Option

The output scores of most of the speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios, which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trial-based calibration (TBC). TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing: 1) a new similarity metric for selecting training data that result in significant gains over the one proposed in the original work; 2) a new option that enables the system to reject a trial when not enough matched data are available for training the calibration model; and 3) the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the Federal Bureau of Investigation multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most of the conditions when matched calibration data are available for selection, and that it can reject most of the trials for which relevant calibration data are unavailable.Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Nandwana, Mahesh Kumar. No especifíca;Fil: McLaren, Mitchell. No especifíca;Fil: Castan, Diego. No especifíca;Fil: Lawson, Aaron. No especifíca