Forensic Face Recognition: A Survey

Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework

Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems

This is the author’s version of a work that was accepted for publication in Forensic Science International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International, Vol 155, Issue 2 (20 December 2005) DOI: 10.1016/j.forsciint.2004.11.007The Bayesian approach provides a unified and logical framework for the analysis of evidence and to provide results in the form of likelihood ratios (LR) from the forensic laboratory to court. In this contribution we want to clarify how the biometric scientist or laboratory can adapt their conventional biometric systems or technologies to work according to this Bayesian approach. Forensic systems providing their results in the form of LR will be assessed through Tippett plots, which give a clear representation of the LR-based performance both for targets (the suspect is the author/source of the test pattern) and non-targets. However, the computation procedures of the LR values, especially with biometric evidences, are still an open issue. Reliable estimation techniques showing good generalization properties for the estimation of the between- and within-source variabilities of the test pattern are required, as variance restriction techniques in the within-source density estimation to stand for the variability of the source with the course of time. Fingerprint, face and on-line signature recognition systems will be adapted to work according to this Bayesian approach showing both the likelihood ratios range in each application and the adequacy of these biometric techniques to the daily forensic work.This work has been partially supported under MCYT Projects TIC2000-1683, TIC2000-1669, TIC2003-09068, TIC2003-08382 and Spanish Police Force ‘‘Guardia Civil’’ Research Program

Assessing the admissibility of a new generation of forensic voice comparison testimony

This article provides a primer on forensic voice comparison (aka forensic speaker recognition), a branch of forensic science in which the forensic practitioner analyzes a voice recording in order to provide an expert opinion that will help the trier-of-fact determine the identity of the speaker. The article begins with an explanation of ways in which human speech varies within and between speakers. It then discusses different technical approaches that forensic practitioners have used to compare voice recordings, and frameworks of reasoning that practitioners have used for evaluating the evidence and reporting its strength. It then discusses procedures for empirical validation of the performance of forensic voice comparison systems. It also discusses the potential influence of contextual bias and ways to reduce this. Building on this scientific foundation, the article then offers analysis, commentary, and recommendations on how courts evaluate the admissibility of forensic voice comparison testimony under the Daubert and Frye standards. It reviews past rulings such as U.S. v. Angleton, 269 F.Supp 2nd 892 (S.D. Tex. 2003) that found expert testimony based on the spectrographic approach inadmissible under Daubert. The article also offers a detailed analysis of the evidence presented in the recent Daubert hearing in U.S. v. Ahmed, et al. 2015 EDNY 12-CR-661, which included testimony based on the newer automatic approach. The scientific testimony proffered in Ahmed is used to illustrate the issues courts are likely to face when considering the admissibility of forensic voice comparison testimony in the future. The article concludes with a discussion of how proponents of forensic voice comparison testimony might meet a reasonably rigorous application of the Daubert standard and thereby ensure that such testimony is sufficiently trustworthy to be used in court

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

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates

Likelihood Ratios for Deep Neural Networks in Face Comparison

In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state-of-the-art free software was preferred over commercial software. Three different open-source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low-quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full-frontal images, which can help the forensic expert in casework

Admissibility of forensic voice comparison testimony in England and Wales

In 2015 the Criminal Practice Directions (CPD) on admissibility of expert evidence in England and Wales were revised. They emphasised the principle that “the court must be satisfied that there is a sufficiently reliable scientific basis for the evidence to be admitted”. The present paper aims to assist courts in understanding from a scientific perspective what would be necessary to demonstrate the validity of testimony based on forensic voice comparison. We describe different technical approaches to forensic voice comparison that have been used in the UK, and critically review the case law on their admissibility. We conclude that courts have been inconsistent in their reasoning. In line with the CPD, we recommend that courts enquire as to whether forensic practitioners have made use of data and analytical methods that are appropriate and adequate for the case under consideration, and that courts require forensic practitioners to empirically demonstrate the level of performance of their forensic voice comparison system under conditions reflecting those of the case under consideration

Empirical test of the performance of an acoustic-phonetic approach to forensic voice comparison under conditions similar to those of a real case

In a 2012 case in New South Wales, Australia, the identity of a speaker on several audio recordings was in question. Forensic voice comparison testimony was presented based on an auditory-acoustic-phonetic-spectrographic analysis. No empirical demonstration of the validity and reliability of the analytical methodology was presented. Unlike the admissibility standards in some other jurisdictions (e.g., US Federal Rule of Evidence 702 and the Daubert criteria, or England & Wales Criminal Practice Directions 19A), Australia's Unified Evidence Acts do not require demonstration of the validity and reliability of analytical methods and their implementation before testimony based upon them is presented in court. The present paper reports on empirical tests of the performance of an acoustic-phonetic-statistical forensic voice comparison system which exploited the same features as were the focus of the auditory-acoustic-phonetic-spectrographic analysis in the case, i.e., second-formant (F2) trajectories in /o/ tokens and mean fundamental frequency (f0). The tests were conducted under conditions similar to those in the case. The performance of the acoustic-phonetic-statistical system was very poor compared to that of an automatic system. © 2017 Elsevier B.V