In this paper, we develop a novel behavioral biometric recognition framework, BlinkAuth, that takes advantage of a user's blinking. BlinkAuth utilizes electrooculogram (EOG) data, (i.e., the electric potential difference between the corneal and retinal sides of the eye), and applies a machine-learning model to achieve user recognition. BlinkAuth works with devices like smart glasses and VR headsets and can be used simultaneously in activities such as driving or cooking. Using JINS MEME, a glasses-type wearable device that can measure EOG, we collected EOG data from 31 participants under various conditions and evaluated the recognition accuracy of BlinkAuth. The results demonstrate that BlinkAuth can achieve high accuracy as a behavioral biometric recognition with an average AUC of 95.8% and an average EER of 9.28%. We developed a system for implementing BlinkAuth for real-time recognition and evaluated the time required for the recognition process and the system's usability with the System Usability Scale (SUS). The results show an overall processing time of approximately 0.6 seconds, including the data measurement time, and an average SUS score of 82.50, which indicates high usability equivalent to rank A in the standard criteria for interpreting SUS scores. Six extensive user experiments and 17 evaluation perspectives reveal that BlinkAuth is highly robust to environmental changes, such as skin moisture and makeup, participant actions, and eye strain conditions, as well as to attacks that imitate the target's blinking.

Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker’s motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker’s motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method’s effectiveness, showing how security measures influence attacker’s motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.

Demographic bias is one of the major challenges for face recognition systems. The majority of existing studies on demographic biases are heavily dependent on specific demographic groups or demographic classifier, making it difficult to address performance for unrecognised groups. This paper introduces "LabellessFace", a novel framework that improves demographic bias in face recognition without requiring demographic group labeling typically required for fairness considerations. We propose a novel fairness enhancement metric called the class favoritism level, which assesses the extent of favoritism towards specific classes across the dataset. Leveraging this metric, we introduce the fair class margin penalty, an extension of existing margin-based metric learning. This method dynamically adjusts learning parameters based on class favoritism levels, promoting fairness across all attributes. By treating each class as an individual in facial recognition systems, we facilitate learning that minimizes biases in authentication accuracy among individuals. Comprehensive experiments have demonstrated that our proposed method is effective for enhancing fairness while maintaining authentication accuracy.

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://ears.fri.uni-lj.si/

With the growth of image on the web, research on hashing which enables high-speed image retrieval has been actively studied. In recent years, various hashing methods based on deep neural networks have been proposed and achieved higher precision than the other hashing methods. In these methods, multiple losses for hash codes and the parameters of neural networks are defined. They generate hash codes that minimize the weighted sum of the losses. Therefore, an expert has to tune the weights for the losses heuristically, and the probabilistic optimality of the loss function cannot be explained. In order to generate explainable hash codes without weight tuning, we theoretically derive a single loss function with no hyperparameters for the hash code from the probability distribution of the images. By generating hash codes that minimize this loss function, highly accurate image retrieval with probabilistic optimality is performed. We evaluate the performance of hashing using MNIST, CIFAR-10, SVHN and show that the proposed method outperforms the state-of-the-art hashing methods.

A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conjunction with template protection schemes to speed up biometric identification, since a biometric index leaks some information about the original feature. In this paper, we propose a novel indexing scheme called “cancelable indexing” to speed up CIRF without losing its security properties. The proposed scheme is based on fast computation of CIRF via low-rank approximation of biometric images and via a minimum spanning tree representation of low-rank matrices in the Fourier domain. We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and thoroughly discuss the security of the proposed scheme. We also demonstrate that it significantly reduces the one-to-many matching time using a finger-vein dataset that includes six fingers from 505 subjects.

The number of victims suffering from crypto ransomware is increasing. Methods for detecting ransomware when it accesses target files or when it uses encrypting APIs have been studied. However, the former method is operated within an analysis sandbox, and the latter method can be avoided if the ransomware uses its own encrypting functions. To protect users, a detection method should be able to detect ransomware in the user's real-time environment and make it difficult for the ransomware to avoid detection. This paper proposes a detection method that satisfies these requirements by using human file-operating characteristics as a whitelist. We evaluate the effectiveness of our prototype method, which inspects the consistency between displayed documents and the user's editing operations.

We analyze the theoretical vulnerability of maximum a posteriori(MAP) speaker adaptation, which is widely used in practical speaker recognition systems. First, we proved that there exist a set of feature vectors, what are called wolves, which can impersonate almost all the registered speakers with probability asymptotically close to 1 with at most two trials. Second, our experiment shows that the wolves with appropriate parameters achieved 0.99 of successful impersonation rate on Spear speaker recognition toolkit with ATR speech database.

Impersonation by impostors is one of the representative security issues on biometric authentication system. A wolf attack is an attack on biometrics system using a wolf that can be falsely accepted as a match with multiple templates. False acceptance rate (FAR) which has been a conventional standard measure to quantify the average error rates of detecting the impersonation has not taken into consideration that impostors could use artefacts instead of templates generated from an individual. The wolf attack probability (WAP) is thus used as a new measure for evaluating the security of biometric authentication. In this paper, we focus on the vulnerability of likelihood-ratio-based biometric verification scheme that is known as optimal similarity measure in terms of average error rates. First, we present theoretical analysis of a likelihood-ratio-based biometric verification system and show the existence of wolf features under the assumption that there is an approximation error between background model and true feature distribution. Second, we propose a new wolf attack scheme that can achieve 60% of WAP. Furthermore, we empirically evaluate the proposed wolf attack using real biometric data from ATR speech database.

Impersonation attack is one of the major security issues of biometric authentication systems. Wolf attacks use a biometric sample such that the similarities between this sample and a number of templates are resulting in high false matches with these templates. In the conventional evaluation with the wolf attack probability (WAP), wolf attacks took advantage of vulnerabilities on the specific matching algorithms, and thereby high WAPs were achieved. However, in actual biometric authentication systems, their algorithm will be black boxes, and artificial samples will be refused by someones's observation or liveness detection; therefore, wolf attacks do not always have theoretical WAPs. We focus on speaker verification systems, and propose a wolf attack that does not depend on matching algorithms and in which people cannot guess whether wolves are artifacts or not. Additionally, we show that the wolf attack became more efficient by creating a wolf for each gender.

We propose the minimum distance entropy (MDE) as a metric of biométrie information content. The MDE is the probability that two biométrie samples correspond exactly expressed in information content and can be calculated through the experiment for interpersonal matching using a set of biométrie samples. This metric makes it possible for certain biometrics not only to be compared with other biometrics but also to be partially compared with personal authentication using passwords, PIN, or other methods in regard to the identification performance or the security. In this paper, we discuss the metric in terms of information theory and show how to evaluate it. Then, as an example, we apply it to a fingerprint system and evaluate fingerprint information content through simulations.

Biometric person authentication has been attracting considerable attention in recent years. Conventional biometric person authentication systems, however, simply store each user's template as-is on the system. If registered templates are not properly protected, the risk arises of template leakage to a third party and impersonation using biometric data restored from a template. We propose a technique that encrypts and stores the user template and uses a “fuzzy vault scheme” to generate secret data from the user template and the query biometric data. It incorporates a measure to prevent the secret data and the user template used to obtain the secret data from being recovered from information stored in the system, and it enables the secret data to be generated from the user's biometric data. In this paper, we introduce this technique and evaluate template security with it by simulating a fingerprint authentication system.