Multibiometric Database Research Articles

A Novel System and Experimental Study for 3D Finger Multibiometrics

Fingers contain various discriminative biometric traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on, which are complementary in identity information. However, only one or a few traits are used in most current research and practical applications, while others are ignored, resulting in degraded recognition performance and vulnerability to forgery. In this paper, we make the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is proposed. To the best of our knowledge, it is the first biometric imaging system capable of capturing almost all finger-based traits. We scanned numerous fingers with this 3D finger imaging system, obtaining external skin images and internal vein images from 6 different views. The proposed 3D finger reconstruction and texture mapping algorithms are then used to generate 3D finger models with skin and vein textures. Second, we establish a benchmark dataset, namely the Large-scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). The LFMB-3DFB contains 695 fingers, and each finger is acquired 10 times, yielding 6 finger skin images and 6 finger vein images for a total of 83,400 images and 6,950 3D finger models. Finally, we design a more scientific and comprehensive evaluation protocol to conduct extensive experimental research and analysis on this database for both subject-independent verification and subject-independent close-set identification tasks. Comprehensive and rigorous experiments for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion on LFMB-3DFB are carried out, and excellent results are achieved. The LFMB-3DFB database will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SCUT-BIP-Lab/LFMB-3DFB</uri> to promote 3D finger multi-biometric research using cutting-edge imaging techniques.

Retrieval performance analysis of multibiometric database using optimized multidimensional spectral hashing based indexing

Demand for efficient retrieval of data from biometric databases is increasing due to its widespread authentication applications that range from e-passport to attendance system. Major research contribution in this area is by using tree based indexing methods and data independent random hashing methods. In this work, the data dependent hashing technique using optimized multidimensional spectral hashing that uses hash table lookup is employed. Palmprint, Iris and Face biometric features are generated using GIST, optimized features are fused based on bio-inspired cuckoo search algorithm and then converted into binary hash code. The compact binary codes representing the fused features form the multibiometric database on which the retrieval performance is analyzed. Simulation results obtained indicate that the hit rate and penetration rate have improved considerably for the desired recognition accuracy.

An efficient and reduced memory indexing approach based on priority rank spectral hashing for multibiometric database

Fast retrieval of data from a multibiometric database is a challenging task as the size of the databases has increased considerably. For the retrieval to be faster, the search space has to be narrowed to a smaller set comprising of nearest neighbours. To achieve this, an appropriate data structure is to be built. Hence a novel priority rank-based spectral hashing algorithm is implemented to enhance the efficiency of indexing in a multibiometric database of iris and palmprints. To improve the matching accuracy, GIST feature extraction is used with weighted feature level fusion. From the experimental results, it is concluded that the proposed indexing algorithm has reduced storage cost by 85%, along with reduced penetration rate, false acceptance rate and false rejection rate. In addition, hit rate has improved by 25% compared to the existing kd tree technique.

An efficient and reduced memory indexing approach based on priority rank spectral hashing for multibiometric database

An efficient and reduced memory indexing approach based on priority rank spectral hashing for multibiometric database

Adaptive fusion of biometric and biographic information for identity de-duplication

Use of biometrics for person identification has increased tremendously over the past decade, e.g., in large scale national identification programs, for law enforcement and border control applications, and social welfare initiatives. For such large scale applications with a diverse target population, unimodal biometric systems, which use a single biometric trait (e.g., fingerprints), are inadequate due to their limited capacity. Multimodal biometric systems, which fuse multiple biometric traits (e.g., fingerprints and face), are required for large-scale identification applications, e.g., de-duplication where the goal is to ensure that the same person does not have two different official credentials (e.g., national ID card) based on different credentials. While multimodal biometric systems offer several advantages (e.g., improvement in recognition accuracy, decrease in failure to enroll rate), they require large enrollment and de-duplication times. This paper proposes an adaptive sequential framework to automatically determine which subset of biometric traits and biographic information is adequate for de-duplication of a given query. An analysis of this strategy is presented on a virtual multi-biometric database of 27,000 subjects (fingerprints from NIST SD14 dataset and face images from the PCSO dataset) along with biographic information sampled from the US census data. Experimental results, using three-fold cross-validation, show that without any loss in de-duplication accuracy, on average, for 63.18% (of a total of 27,000) of the queries, only fingerprint capture is adequate, for an additional 28.69% of queries, both fingerprint and face are required, and only 8.13% of the queries needed biographic information in addition to fingerprint and face.

An open framework for distributed biometric authentication in a web environment

Large-scale deployment of biometric systems for web-based services has to tackle technological issues related to security, interoperability and accuracy, and social issues related to privacy protection and biometric acquisition process acceptance. The variety of biometric traits, capturing devices, targeted populations and working scenarios makes the development of a universal solution for all-purpose deployment quite a difficult task. This paper describes the design, implementation and applicability of an open framework for distributed biometric authentication oriented to access-control in web environment. The open principle makes this framework a novel and practical development tool for testing and integrating biometric algorithms and devices from third parties. Special attention has been paid to security and interoperability standards to ease concurrent integration and testing of biometric trait matchers developed by different laboratories or companies. Finally, in order to demonstrate the versatility and usability of the framework, we describe the construction process for a distributed multibiometric database acquisition tool based on this framework.