Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Adapted user-dependent multimodal biometric authentication exploiting general information

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 26.16 (2005): 2628 – 2639, DOI: 10.1016/j.patrec.2005.06.008A novel adapted strategy for combining general and user-dependent knowledge at the decision-level in multimodal biometric authentication is presented. User- independent, user-dependent, and adapted fusion and decision schemes are com- pared by using a bimodal system based on ¯ngerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this pa- per. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.This work has been supported by the Spanish Ministry for Science and Tech- nology under projects TIC2003-09068-C02-01 and TIC2003-08382-C05-01

Multiple classifiers in biometrics. part 1: Fundamentals and review

We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems. Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance themThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE

Multiple classifiers in biometrics. Part 2: Trends and challenges

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key roleThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of this work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE

Multi-Modal Biometrics: Applications, Strategies and Operations

The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models