arXiv subjects

Kenta Takahashi

Publications and source records attributed to Kenta Takahashi.

8 recordsLinked to original sources

arXiv2026

MAGE-Vein: Multi-Instance Age and Gender Estimation from Finger Vein Images

Age estimation from finger vein images has been widely considered impractical due to severe demographic biases in public datasets and physiological confounding factors like gender. To overcome these limitations, we propose MAGE-Vein, a novel multi-instance, multi-task learning framework. Our approach extracts robust structural aging signs by employing a hybrid feature-level fusion of three fingers, effectively suppressing local imaging noise. Furthermore, simultaneous optimization of gender classification conditions the network to effectively eliminate gender-specific vascular variations. Evaluated on a demographically balanced dataset of 402 subjects, MAGE-Vein achieves a mean absolute error of 6.12 years and a correlation of 0.880. Our results not only overturn the conventional consensus regarding the limitations of the finger vein modality but also demonstrate that previous estimation failures were primarily artifacts of biased public datasets. Our code is available at https://github.com/gsisaoki/MAGE-Vein.

cs.CV↗

arXiv2024

A Linear-Time Algorithm for the Closest Vector Problem of Triangular Lattices

Fuzzy Extractor (FE) and Fuzzy Signature (FS) are useful schemes for generating cryptographic keys from fuzzy data such as biometric features. Several techniques have been proposed to implement FE and FS for fuzzy data in an Euclidean space, such as facial feature vectors, that use triangular lattice-based error correction. In these techniques, solving the closest vector problem (CVP) in a high dimensional (e.g., 128--512 dim.) lattice is required at the time of key reproduction or signing. However, solving CVP becomes computationally hard as the dimension $n$ increases. In this paper, we first propose a CVP algorithm in triangular lattices with $O(n \log n)$-time whereas the conventional one requires $O(n^2)$-time. Then we further improve it and construct an $O(n)$-time algorithm.

cs.CR↗

arXiv2024

Relativistic two-wave resonant acceleration of electrons at large-amplitude standing whistler waves during laser-plasma interaction

The interaction between a thin foil target and a circularly polarized laser light injected along an external magnetic field is investigated numerically by particle-in-cell simulations. A standing wave appears at the front surface of the target, overlapping the injected and partially reflected waves. Hot electrons are efficiently generated at the standing wave due to the relativistic two-wave resonant acceleration if the magnetic field amplitude of the standing wave is larger than the ambient field. A bifurcation occurs in the gyration motion of electrons, allowing all electrons with non-relativistic velocities to acquire relativistic energy through the cyclotron resonance. The optimal conditions for the highest energy and the most significant fraction of hot electrons are derived precisely through a simple analysis of test-particle trajectories in the standing wave. Since the number of hot electrons increases drastically by many orders of magnitude compared to the conventional unmagnetized cases, this acceleration could be a great advantage in laser-driven ion acceleration and its applications.

physics.plasm-ph↗

arXiv2024

IPFed: Identity protected federated learning for user authentication

With the development of laws and regulations related to privacy preservation, it has become difficult to collect personal data to perform machine learning. In this context, federated learning, which is distributed learning without sharing personal data, has been proposed. In this paper, we focus on federated learning for user authentication. We show that it is difficult to achieve both privacy preservation and high accuracy with existing methods. To address these challenges, we propose IPFed which is privacy-preserving federated learning using random projection for class embedding. Furthermore, we prove that IPFed is capable of learning equivalent to the state-of-the-art method. Experiments on face image datasets show that IPFed can protect the privacy of personal data while maintaining the accuracy of the state-of-the-art method.

cs.CV↗

arXiv2021

Toward Evaluating Re-identification Risks in the Local Privacy Model

LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local model. However, there are scenarios in which the adversary wants to perform re-identification attacks to link the obfuscated data to users in this model. LDP can cause excessive obfuscation and destroy the utility in these scenarios because it is not designed to directly prevent re-identification. In this paper, we propose a measure of re-identification risks, which we call PIE (Personal Information Entropy). The PIE is designed so that it directly prevents re-identification attacks in the local model. It lower-bounds the lowest possible re-identification error probability (i.e., Bayes error probability) of the adversary. We analyze the relation between LDP and the PIE, and analyze the PIE and utility in distribution estimation for two obfuscation mechanisms providing LDP. Through experiments, we show that when we consider re-identification as a privacy risk, LDP can cause excessive obfuscation and destroy the utility. Then we show that the PIE can be used to guarantee low re-identification risks for the local obfuscation mechanisms while keeping high utility.

cs.CR↗

arXiv2021

Revisiting Fuzzy Signatures: Towards a More Risk-Free Cryptographic Authentication System based on Biometrics

Biometric authentication is one of the promising alternatives to standard password-based authentication offering better usability and security. In this work, we revisit the biometric authentication based on "fuzzy signatures" introduced by Takahashi et al. (ACNS'15, IJIS'19). These are special types of digital signatures where the secret signing key can be a "fuzzy" data such as user's biometrics. Compared to other cryptographically secure biometric authentications as those relying on fuzzy extractors, the fuzzy signature-based scheme provides a more attractive security guarantee. However, despite their potential values, fuzzy signatures have not attracted much attention owing to their theory-oriented presentations in all prior works. For instance, the discussion on the practical feasibility of the assumptions (such as the entropy of user biometrics), which the security of fuzzy signatures hinges on, is completely missing. In this work, we revisit fuzzy signatures and show that we can indeed efficiently and securely implement them in practice. At a high level, our contribution is threefold: (i) we provide a much simpler, more efficient, and direct construction of fuzzy signature compared to prior works; (ii) we establish novel statistical techniques to experimentally evaluate the conditions on biometrics that are required to securely instantiate fuzzy signatures; and (iii) we provide experimental results using a real-world finger-vein dataset to show that finger-veins from a single hand are sufficient to construct efficient and secure fuzzy signatures. Our performance analysis shows that in a practical scenario with 112-bits of security, the size of the signature is 1256 bytes, and the running time for signing/verification is only a few milliseconds.

cs.CR↗

arXiv2019

PDH : Probabilistic deep hashing based on MAP estimation of Hamming distance

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.

cs.CV↗

arXiv2018

Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification

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.

cs.CV↗