Performance Of Fingerprint Recognition System Research Articles

Securing Multiple Biometric Data Using SVD and Curvelet-Based Watermarking

The security and privacy of biometric data in multibiometric systems has become a hot research topic. In this paper, a singular value decomposition (SVD) and fast discrete curvelet transform (FDCuT)-based watermarking scheme for authenticity of fingerprint image using watermark speech signal has been proposed and analyzed. This scheme also provides security to watermark speech signal, which is inserted into the fingerprint image. This proposed scheme has a number of steps including fingerprint image authentication using watermark speech signal. The human speech signal is taken as secret watermark information and inserting into the human fingerprint image in the proposed scheme. The singular value of high frequency curvelet coefficients of the host fingerprint image is modified according to watermark speech signal to get secured and watermarked fingerprint image. The analysis results show that the performance of fingerprint recognition system is not affected by inserted watermark speech signal into host fingerprint image.

지문인식 시스템의 효율성에 관한 품질평가 모델

지문인식시스템은 사용자의 지문을 전자적으로 읽어 미리 입력된 데이터와 비교해 본인 여부를 판별하여 사용자의 신분을 확인하는 시스템이다. 지문인식시스템의 성능은 지문 인식에 걸리는 시간이나 정확도 등에 좌우된다. 본 논문에서는 지문인식시스템의 품질 수준을 평가하기 위해 ISO 품질평가 표준을 기반으로 효율성에 관한 평가모델을 구축하였다. 본 연구를 통해 표준에 입각한 평가기준 구축과 활용에 기여할 수 있을 것으로 기대한다. The Fingerprint recognition system is a system which identify the user's identify by verifying user's fingerprint and prepared data. The performance of fingerprint recognition system is dependent on 'fingerprint recognition time' and 'fingerprint recognition accuracy' and so on. In this paper, we developed a evaluation model about efficiency based on ISO quality evaluation standard for evaluating of quality level of fingerprint recognition system. We expect to contribute to construct and use of evaluation criteria based on quality evaluation standard by this study.

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors.