A framework for forensic face recognition based on recognition performance calibrated for the quality of image pairs

Recently, it has been shown that performance of a face recognition system depends on the quality of both face images participating in the recognition process: the reference and the test image. In the context of forensic face recognition, this observation has two implications: a) the quality of the trace (extracted from CCTV footage) constrains the performance achievable using a particular face recognition system; b) the quality of the suspect reference set (to which the trace is matched against) can be judiciously chosen to approach optimal recognition performance under such a constraint. Motivated by these recent findings, we propose a framework for forensic face recognition that is based on calibrating the recognition performance for the quality of pairs of images. The application of this framework to several mock-up forensic cases, created entirely from the MultiPIE dataset, shows that optimal recognition performance, under such a constraint, can be achieved by matching the quality (pose, illumination, and, imaging device) of the reference set to that of the trace. This improvement in recognition performance helps reduce the rate of misleading interpretation of the evidence

A review of calibration methods for biometric systems in forensic applications

When, in a criminal case there are traces from a crime scene - e.g., finger marks or facial recordings from a surveillance camera - as well as a suspect, the judge has to accept either the hypothesis \emph{$H_{p}$} of the prosecution, stating that the trace originates from the subject, or the hypothesis of the defense \emph{$H_d$}, stating the opposite. The current practice is that forensic experts provide a degree of support for either of the two hypotheses, based on their examinations of the trace and reference data - e.g., fingerprints or photos - taken from the suspect. There is a growing interest in a more objective quantitative support for these hypotheses based on the output of biometric systems instead of manual comparison. However, the output of a score-based biometric system is not directly suitable for quantifying the evidential value contained in a trace. A suitable measure that is gradually becoming accepted in the forensic community is the Likelihood Ratio (LR) which is the ratio of the probability of evidence given \emph{$H_p$} and the probability of evidence given \emph{$H_d$}. In this paper we study and compare different score-to-LR conversion methods (called calibration methods). We include four methods in this comparative study: Kernel Density Estimation (KDE), Logistic Regression (Log Reg), Histogram Binning (HB), and Pool Adjacent Violators (PAV). Useful statistics such as mean and bias of the bootstrap distribution of \emph{LRs} for a single score value are calculated for each method varying population sizes and score location

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

Two-step calibration method for multi-algorithm score-based face recognition systems by minimizing discrimination loss

We propose a new method for combining multi-algorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its goal is to minimize discrimination loss. For synthetic and real databases (NIST-face and Face3D) we will show that our method is accurate and reliable using the cost of log likelihood ratio and the information-theoretical empirical cross-entropy (ECE)

Score based procedures for the calculation of forensic likelihood ratios - scores should take account of both similarity and typicality

Score based procedures for the calculation of forensic likelihood ratios are popular across different branches of forensic science. They have two stages, first a function or model which takes measured features from known-source and questioned-source pairs as input and calculates scores as output, then a subsequent model which converts scores to likelihood ratios. We demonstrate that scores which are purely measures of similarity are not appropriate for calculating forensically interpretable likelihood ratios. In addition to taking account of similarity between the questioned-origin specimen and the known-origin sample, scores must also take account of the typicality of the questioned-origin specimen with respect to a sample of the relevant population specified by the defence hypothesis. We use Monte Carlo simulations to compare the output of three score based procedures with reference likelihood ratio values calculated directly from the fully specified Monte Carlo distributions. The three types of scores compared are: 1. non-anchored similarity-only scores; 2. non-anchored similarity and typicality scores; and 3. known-source anchored same-origin scores and questioned-source anchored different-origin scores. We also make a comparison with the performance of a procedure using a dichotomous “match”/“non-match” similarity score, and compare the performance of 1 and 2 on real data

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