• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.05c
• /
• pp.1921-1924
• /
• 2003

지문 인식(identification)은 사용자 등록 과정과 사용자 인식 과정으로 수행된다. 사용자 등록 과정은 데이터베이스에 사용자의 지문 정보를 저장하는 과정이고 인식 과정은 입력된 사용자의 지문정보에 대하여 유사한 후보자 목륵을 작성하는 과정이다. 지문 인식은 데이터베이스에 저장된 사용자 지문 정보를 전체적으로 검색할 경우 오랜 수행 시간이 요구되므로 이러한 문제점을 해결하기 위하여, 본 논문에서는 기하학적 해싱(geometric hashing) 알고리즘을 이용하여 지문 인식을 수행하며 지문 인식을 병렬적으로 처리하기 위해 데이터베이스의 구조를 k 비트-레벨 표현으로 나타내었다. 또한, 실시간으로 지문 인식을 처리하기 위해 지문 인식을 위한 전용 하드웨어를 설계하였다. 설계된 하드웨어는 등록자 수의 증가에 따른 우수한 확장성을 보이며 병렬적으로 처리되므로 고속 실시간 처리가 가능하다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.1
• /
• pp.11-21
• /
• 2008

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. This construct aims to secure critical data(e.g., secret key) with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint, and some implementations results for fingerprint have been reported. However, the previous results had some limitation of the provided security due to the limited numbers of chaff data fer hiding real fingerprint data. In this paper, we propose an approach to provide both the automatic alignment of fingerprint data and higher security by using a 3D geometric hash table. Based on the experimental results, we confirm that the proposed approach of using the 3D geometric hash table with the idea of the fuzzy vault can perform the fingerprint verification securely even with more chaff data included.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2006.05a
• /
• pp.889-892
• /
• 2006

생체정보를 이용한 사용자 인증시스템은 편리함과 동시에 강력한 보안을 제공할 수 있다. 그러나 사용자 인증을 위해 저장된 중요한 생체정보가 타인에게 도용된다면 심각한 문제를 일으킨다. 따라서 타인에게 유출되더라도 재사용이 불가능하도록 하기 위하여 사용자의 생체정보에 역변환이 불가능한 함수를 적용하여 저장하고 변환된 상태에서 인증 과정을 수행할 수 있는 방법이 필요하다. 본 논문에서는 최근 지문 템플릿(Fingerprint Template)을 보호하기 위해 암호학적 방법으로 연구되어지고 있는 퍼지볼트(Fuzzy Vault) 이론을 이용하여 안전하게 지문 템플릿을 보호하는 방법을 제안한다. 특히, 지문 템플릿에 퍼지볼트 이론을 단순 적용할 경우 기준점 부재로 인하여 야기되는 지문 정렬(alignment) 문제를 해결하기 위하여 기하학적 해싱(Geometric Hashing)방법을 사용하였다. 실험을 통하여 제안한 지문 퍼지볼트(Fuzzy Fingerprints Vault) 기법은 추가적인 정보없이 변환된 도메인에서 자동으로 지문 정렬을 수행하여 안전한 지문 템플릿 보호 및 인증이 가능하다는 것을 확인하였다.

• The Journal of Korean Association of Computer Education
• /
• v.12 no.4
• /
• pp.57-64
• /
• 2009

Protein structure comparison is a task that requires a lot of computing resources because many atoms in proteins need to be processed. To address the issue, Grid computing environment has been employed for processing time-consuming jobs in a distributed manner. However, controling the Grid computing environment may not be easy for non-experts. In this paper, we present a JXTA-based system for protein structure comparison that can be easily controled by non-experts. To search proteins similar to a query protein, the geometric hashing algorithm that consists of preprocessing and recognition was employed. Experimental results indicate that the system can find the correct protein structure for a given query protein structure and the proposed system can be easily extended to solve the protein docking problem. It is expected that the proposed system can be useful for non-experts, especially users who do not have sophisticated knowledge of distributed systems in general such as college students who major in biology or chemistry.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.1
• /
• pp.63-70
• /
• 2009

Recently, in the security token based authentication system, there is an increasing trend of using fingerprint for the token holder verification, instead of passwords. However, the security of the fingerprint data is particularly important as the possible compromise of the data will be permanent. In this paper, we propose an approach for secure fingerprint verification by distributing both the secret and the computation based on the fuzzy vault(a cryptographic construct which has been proposed for crypto-biometric systems). That is, a user fingerprint template which is applied to the fuzzy vault is divided into two parts, and each part is stored into a security token and a server, respectively. At distributing the fingerprint template, we consider both the security level and the verification accuracy. Then, the geometric hashing technique is applied to solve the fingerprint alignment problem, and this computation is also distributed over the combination of the security token and the server in the form of the challenge-response. Finally, the polynomial can be reconstructed from the accumulated real points from both the security token and the server. Based on the experimental results, we confirm that our proposed approach can perform the fuzzy vault-based fingerprint verification more securely on a combination of a security token and a server without significant degradation of the verification accuracy.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.12
• /
• pp.519-524
• /
• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.