Estimation of cylinder quality measures from quality maps for Minutia-Cylinder Code based latent fingerprint matching

Poor quality of fingerprint data is one of the major problems concerning latent fingerprint matching in forensic applications. Local quality of fingerprint plays a very important role in this application field to ensure high recognition performance. Al- though big progress has been made in matching of fingerprints using local minutiae descriptors, in particular Minutia Cylinder- Code (MCC), automatic latent fingerprint matching continues to be a challenge. Previously we proposed a matching algo- rithm that uses minutiae information encoded by MCC with in- tegrated local quality measures associated to each MCC called cylinder quality measures. In our previous work, cylinder qual- ity measures for latent case have been proposed by combining the subjective qualities of individual minutiae involved. In this paper, we propose an alternative method to estimate the cylin- der quality measures directly from fingerprint quality maps, in particular ridge clarity maps, by taking into account the num- ber of involving minutiae as well. Integration of MCC with the proposed cylinder quality measures was evaluated through ex- periments on the latent fingerprint database NIST SD27, which show clear improvements in the identification performance of latent fingerprints of ugly quality

Introduction of cylinder quality measure into Minutia Cylinder-Code based fingerprint matching

Poor data quality is responsible for many or even most matching errors in fingerprint recognition systems. It became obvious that particular effort is needed in adaptation of the state-of-the-art minutiae-based fingerprint matching techniques to real-world conditions using quality measures. In this paper, we address a challenging problem of how to associate local quality measures to local minutiae descriptors, in particular Minutia Cylinder-Code (MCC), in order to obtain better recognition rates. Firstly, we introduce a new local quality measure, called Cylinder Quality Measure (CQM), corresponding to each MCC descriptor by combining the qualities of individual minutiae involved. Then, we propose a method for incorporating such quality measures into fingerprint matching through a quality-based relaxation procedure. Our experiments on the FVC2002 (DB1 and DB3) and FVC2004 (DB3) databases demonstrate that integrating the cylinder quality measure through the proposed procedure improves the overall matching performance comparing to the state-of-the-art MCC based fingerprint matching algorithms

Embedding Local Quality Measures in Minutiae-Based Biometric Recognition

Degradation in data quality is still a main source of errors in the modern biometric recognition systems. However, the data quality can be embedded in the recognition methods at global and local levels to build more accurate biometric systems. Local quality measures represent the quality of local parts within a biometric sample. They are either combined into a global quality measure or directly embedded into the recognition techniques. Minutiae-based comparison is the main and the most common technique used for fingerprint recognition and high-resolution palmprint recognition in various security and forensic applications. The focus of this thesis is mainly on direct incorporation of the local quality measures into the state-of-the-art minutiae-based recognition methods, particularly those based on Minutiae Cylinder-Code (MCC). Firstly, we introduce cylinder quality measures as a new type of local quality measures associated with the local minutiae descriptors. Then, we propose several methods for incorporating such local quality measures into the biometric systems, in order to improve their recognition performance. Among them is a novel and efficient quality-based consolidation method for embedding minutiae quality and cylinder quality measures in MCC based comparison methods. We also propose a supervised embedding method based on a binary classification model, which requires labeled minutiae for training. Finally, we apply a variant of the proposed consolidation method for the challenging case of latent fingerprint and palmprint identification with embedded subjective and objective minutiae quality

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学位の種別:課程博士University of Tokyo(東京大学