Impostor Distributions Research Articles

Fair Face Verification by Using Non-Sensitive Soft-Biometric Attributes

Facial recognition has been shown to have different accuracy for different demographic groups. When setting a threshold to achieve a specific False Match Rate (FMR) on a mixed demographic impostor distribution, some demographic groups can experience a significantly worse FMR. To mitigate this, some authors have proposed to use demographic-specific thresholds. However, this can be impractical in an operational scenario, as it would either require users to report their demographic group or the system to predict the demographic group of each user. Both of these options can be deemed controversial since the demographic group is a sensitive attribute. Further, this approach requires listing the possible demographic groups, which can become controversial in itself. We show that a similar mitigation effect can be achieved using non-sensitive predicted soft-biometric attributes. These attributes are based on the appearance of the users (such as hairstyle, accessories, and facial geometry) rather than how the users self-identify. Our experiments use a set of 38 binary non-sensitive attributes from the MAAD-Face dataset. We report results on the Balanced Faces in the Wild dataset, which has a balanced number of identities by race and gender. We compare clustering-based and decision-tree-based strategies for selecting thresholds. We show that the proposed strategies can reduce differential outcomes in intersectional groups twice as effectively as using gender-specific thresholds and, in some cases, are also better than using race-specific thresholds.

Efficient Implementation of Radon Transform and Encryption Techniques for Cancelable Speaker Identification

This paper introduces three cancelable speaker identification techniques based on the spectrogram estimation of speech signals subjected to either chaotic encryption process, or RSA algorithm in addition to Radon transform to produce cancelable templates instead of the original speech signals. The resulting transformed versions of the voice biometrics are stored in the server instead of the original biometrics. Therefore, the users' privacy can be protected well. It is evident from the obtained results that the proposed techniques are secure, reliable and practical. They have good encryption and ability to generate cancelable templates. These characteristics lead to good performance. The proposed cancelable speaker identification techniques are evaluated under the influence of Additive White Gaussian Noise (AWGN) with different strengths. This makes them more accurate in identifying the users and also more resistant to attack attempts. In addition, security is enhanced through maintaining the confidentiality of the processed data. In the experimental results, evaluation metrics such as Equal Error Rate (EER), False Rejection Rate (FRR), and False Acceptance Rate (FAR) are used to assess the performance of the proposed techniques. In addition, the genuine, impostor distributions, Receiver Operating Characteristic (ROC) curve and area under the ROC curve for the proposed techniques are estimated for better evaluation and comparison.

Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone

Face recognition technology has recently become controversial over concerns about possible bias due to accuracy varying based on race or skin tone. We explore three important aspects of face recognition technology related to this controversy. Using two different deep convolutional neural network face matchers, we show that for a fixed decision threshold, the African-American image cohort has a higher false match rate (FMR), and the Caucasian cohort has a higher false nonmatch rate. We present an analysis of the impostor distribution designed to test the premise that darker skin tone causes a higher FMR, and find no clear evidence to support this premise. Finally, we explore how using face recognition for one-to-many identification can have a very low false-negative identification rate and still present concerns related to the false-positive identification rate. Both the ArcFace and VGGFace2 matchers and the MORPH dataset used in our experiments are available to the research community so that others should be able to reproduce or reanalyze our results.

Synthesis and Evaluation of High Resolution Hand-Prints

This paper introduces a novel method for the generation of high-resolution synthetic hand-print images. Specific traits, such as fingerprint, palmprint, and hand-shape, are synthesized to obtain a whole hand-print. Each trait is generated by a methodology that mimics the nature of the corresponding biometric data and their main degrees of freedom. The biometric traits are then integrated into a single high-resolution realistic image. A quantitative validation of the obtained patterns is carried out in the context of minutiae matching by comparing genuine and impostor distributions between synthetic and real hand-prints. The proposed approach also proved to be useful for algorithm training/optimization.

An Analysis of IrisCode

IrisCode is an iris recognition algorithm developed in 1993 and continuously improved by Daugman. It has been extensively applied in commercial iris recognition systems. IrisCode representing an iris based on coarse phase has a number of properties including rapid matching, binomial impostor distribution and a predictable false acceptance rate. Because of its successful applications and these properties, many similar coding methods have been developed for iris and palmprint identification. However, we lack a detailed analysis of IrisCode. The aim of this paper is to provide such an analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. Our analysis demonstrates that IrisCode is a clustering algorithm with four prototypes; the locus of a Gabor function is a 2-D ellipse with respect to a phase parameter and can be approximated by a circle in many cases; Gabor function can be considered as a phase-steerable filter and the bitwise hamming distance can be regarded as a bitwise phase distance. We also discuss the theoretical foundation of the impostor binomial distribution. We use this analysis to develop a precise phase representation which can enhance accuracy. Finally, we relate IrisCode and other coding methods.

Error rate equations for the general biometric system

We derive equations for false-match and false-nonmatch error-rate prediction for the general M-to-N biometric identification system, under the simplifying, but limiting, assumption of statistical independence of all errors. For systems with large N, error rates are shown to be linked to the hardware processing speed through the system penetration coefficient and the throughput equation. These equations are somewhat limited in their ability to handle sample-dependent decision policies and are shown to be consistent with previously published cases for verification and identification. Applying parameters consistent with the Philippine Social Security System benchmark test results for AFIS vendors, we establish that biometric identification systems can be used in populations of 100 million people. Development of more generalized equations, accounting for error correlation and general sample-dependent thresholds, establishing confidence bounds, and substituting the inter-template for the impostor distribution under the template generating policy remain for future study.