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

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

Does Demutualization Spur Liquidity?

Purpose: The literature on demutualization is confined to efficiency and social welfare issues. Little empirical literature exists on the effect of demutualization on listed firms. This study examines the impact of demutualization on the liquidity of listed firms’ stocks. Methodology: It empirically investigates how the liquidity of listed firms’ stocks is affected by demutualization. Analyzing data of 137 non-financial firms listed on the Pakistan Stock Exchange for 2005 to 2017, we employ fixed effect regression to test the hypotheses. Findings: We find that demutualization has significantly improved liquidity. We analyze all three dimensions of liquidity that are the trading activity, market impact, and transaction cost. We find that demutualization increases trading activity, improve market depth, and has reduced the transaction cost.  Implications: Our findings suggest that demutualization is beneficial not only for listed firms but also for its shareholders as all three dimensions of liquidity are improved by demutualization. Stock exchanges that are not demutualized and are facing liquidity problem, can be improved by changing its structure from mutual to demutualized. Originality: Prior literature focuses on the impact of demutualization on the stock market or social welfare. There is scares research on the effect of demutualization of the listed firm. This study fills this gap by analyzing the impact of demutualization on listed firms' liquidity in a developing economy, such as Pakistan

Computation of likelihood ratio from small sample set of within-source variability

In this paper we describe a new method of likelihood ratio computation for score-based biometric recognition systems given a small number of samples in within-source variability dataset. Generally the number of samples in within-source variability dataset is less than the number of samples in between-source variability dataset and therefore the probability density function (pdf) of within-source variability dataset cannot be estimated reliably compared to the pdf of between-source variability. The proposed method estimates the pdf of within-source variability from estimates of the within-source variability mean and variance and the pdf of between-source variability by minimizing the Kullback-Leibler distance [1] of the pdf of the within-source variability to that of the between-source variability given within-source variability mean and variance. It thus finds a conservative estimate of the pdf of within-source variability. Working out this optimization problem results in an log likelihood ration that is a second order polynomial of a given score value. We apply this approach of likelihood ratio computation in the area of face recognition. An existing commercial face recognition system [2] is used to obtain scores for the sets of within-source variability and between-source variability from a set of image data taken from SCFace database [3]. It contains images taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. For each subject, there are also mug shots taken in same conditions as would be expected for any law enforcement or national security use. We explore the feasibility of using an existing biometric face recognition system in forensic application by discussing some specific cases in forensic framework