Genuine Acceptance Rate Research Articles

Fingerprint-Based Fuzzy Vault: Implementation and Performance

Reliable information security mechanisms are required to combat the rising magnitude of identity theft in our society. While cryptography is a powerful tool to achieve information security, one of the main challenges in cryptosystems is to maintain the secrecy of the cryptographic keys. Though biometric authentication can be used to ensure that only the legitimate user has access to the secret keys, a biometric system itself is vulnerable to a number of threats. A critical issue in biometric systems is to protect the template of a user which is typically stored in a database or a smart card. The fuzzy vault construct is a biometric cryptosystem that secures both the secret key and the biometric template by binding them within a cryptographic framework. We present a fully automatic implementation of the fuzzy vault scheme based on fingerprint minutiae. Since the fuzzy vault stores only a transformed version of the template, aligning the query fingerprint with the template is a challenging task. We extract high curvature points derived from the fingerprint orientation field and use them as helper data to align the template and query minutiae. The helper data itself do not leak any information about the minutiae template, yet contain sufficient information to align the template and query fingerprints accurately. Further, we apply a minutiae matcher during decoding to account for nonlinear distortion and this leads to significant improvement in the genuine accept rate. We demonstrate the performance of the vault implementation on two different fingerprint databases. We also show that performance improvement can be achieved by using multiple fingerprint impressions during enrollment and verification.

Orientation feature for fingerprint matching

This paper describes a fingerprint verification algorithm based on the orientation field. The orientation field of a fingerprint image has also been used for image alignment. Area around the core point has been employed as an area of interest for determining the orientation feature map. The algorithm has been tested on two databases (database available from University of Bologna, Biometrics Laboratory and FVC2002). The performance of the algorithm is measured in terms of receiver operating characteristics (ROC). For the University of Bologna database, at ∼ 0 % false acceptance rate (FAR) the genuine acceptance rate (GAR) observed is ∼ 78 % and at ∼ 11 % FAR, GAR is ∼ 97 % . For the FVC2002 database at ∼ 0 % FAR the GAR observed is 75 % and at ∼ 18 % FAR, GAR is 93 % . Proposed algorithm yields better GAR at low FAR with reduced computational complexity. Because of simplicity in computations the algorithm can be easily implemented as an embedded automatic fingerprint identification system (AFIS).

Online Palmprint Identification System for Civil Applications

In this paper, a novel biometric identification system is presented to identify a person’s identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system. The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuine acceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017% and a false rejection rate, 0.86%. The execution time for the whole process including image collection, preprocessing, feature extraction and matching is less than 1 second.

특징점의 연결정보를 이용한 지문인식

지문영상의 품질 향상과 특징점 정합은 자동 지문인식 시스템의 중요한 두 단계이다. 본 논문에서는 특징점의 연결정보를 사용한 지문인식 기법을 제안한다. 인식 과정은 전처리와 특징점 추출, 그리고 특징점 pairing을 기반으로 한 정합의 세 단계로 이루어져 있다. 정확성을 위해 세선화된 이미지로부터 지문의 특징점을 추출한 후에, 특징점의 연결정보를 사용한 정합과정을 소개한다. 특징점 정합과정에서 연결정보를 사용하는 것은 간단하지만 정확한 방법이며, 두 지문의 비교단계에서 낮은 비용으로 기준 특징점 쌍을 선택하는 문제를 해결해 준다. 알고리즘은 지문의 회전과 이동에 무관하다. 정합 알고리즘은 반도체 칩방식 지문 입력장치로부터 획득한 500개의 지문영상으로 실험하였으며, 실험 결과는 기존 방법보다 오인식율은 줄어들고 정확도는 증가하였음을 보여준다. Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we propose a fingerprint recognition technique by using minutiae linking information. Recognition process have three steps ; preprocessing, minutiae extraction, matching step based on minutiae pairing. After extracting minutiae of a fingerprint from its thinned image for accuracy, we introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection with low cost in comparison stage of two fingerprints. This algorithm is invariable to translation and rotation of fingerprint. The matching algorithm was tested on 500 images from the semiconductor chip style scanner, experimental result revealed the false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

A hybrid fingerprint matcher

Most fingerprint matching systems rely on the distribution of minutiae on the fingertip to represent and match fingerprints. While the ridge flow pattern is generally used for classifying fingerprints, it is seldom used for matching. This paper describes a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the ridge feature map. The ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. The proposed technique has the following features: (i) the entire image is taken into account while constructing the ridge feature map; (ii) minutiae matching is used to determine the translation and rotation parameters relating the query and the template images for ridge feature map extraction; (iii) filtering and ridge feature map extraction are implemented in the frequency domain thereby speeding up the matching process; (iv) filtered query images are catched to greatly increase the one-to-many matching speed. The hybrid matcher performs better than a minutiae-based fingerprint matching system. The genuine accept rate of the hybrid matcher is observed to be ∼10% higher than that of a minutiae-based system at low FAR values. Fingerprint verification (one-to-one matching) using the hybrid matcher on a Pentium III, 800 MHz system takes ∼1.4 s , while fingerprint identification (one-to-many matching) involving 1000 templates takes ∼0.2 s per match.