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

Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the vein patterns in fingers, and it has the benefit of being difficult to counterfeit. Due to its high level of security, durability, and performance history, finger vein recognition captures our attention as one of the most significant authentication methods available today. Using a mixed deep learning approach, we investigate the challenge of identifying the finger vein sensor model. Thus far, we use Traditional LSTM architectures for this biometric modality. This work also suggests a brand-new hybrid architecture that shines due to its compactness and a merging with the LSMT layer to be taught. In the experiment, original samples as well as the region of interest data from eight freely available FV-USM datasets are employed. The standard LSTM-based strategy is preferable and produced better outcomes, as seen by the comparison with the earlier approaches. Moreover, the results show that the hybrid CNN and LSTM networks may be used to improve vein detection performance

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden

Palm Vein Identification Based on Hybrid Feature Selection Model

Palm Vein Identification (PVI) is a modern biometric security technique used for enhancing security and authentication systems. The key characteristics of palm vein patterns include its uniqueness to each individual, its unforgettability, non-intrusiveness and its ability for disallowing unauthorized persons. However, the extracted features from the palm vein patterns are huge with high redundancy. In this paper, we propose a combined model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) that feeds wrapper model with an optimal subset of features to enhance the prediction accuracy of -palm vein patterns. The 2D-DWT extract features from palm vein images, using the PCA to reduce the redundancy in palm vein features. The system has been trained to select high recognition features based on the wrapper model. The proposed system uses four classifiers as an objective function to determine PVI which include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Naïve Bayes (NB). The empirical results proved that the proposed model has the best results with SVM. Moreover, our proposed 2D-DWTPP model has been evaluated and the results show remarkable efficiency in comparison with AlexNet and other classifiers without feature selection. Experimentally, the proposed model has better accuracy as reflected by 98.65% whereas AlexNet has 63.5% accuracy and the classifier without feature selection process has 78.79% accuracy

Security challenges and solutions for e-business

The advantages of economic growth and increasing ease of operation afforded by e-business and e-commerce developments are unfortunately matched by growth in cyber attacks. This paper outlines the common attacks faced by e-business and describes the defenses that can be used against them. It also reviews the development of newer security defense methods. These are: (1) biometrics for authentication; parallel processing to increase power and speed of defenses; (2) data mining and machine learning to identify attacks; (3) peer-to-peer security using blockchains; 4) enterprise security modelling and security as a service; and (5) user education and engagement. The review finds overall that one of the most prevalent dangers is social engineering in the form of phishing attacks. Recommended counteractions include education and training, and the development of new machine learning and data sharing approaches so that attacks can be quickly discovered and mitigated

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

A Review of Voice-Base Person Identification: State-of-the-Art

Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control