In this study, an efficient human authentication method is proposed which utilises finger texture (FT) patterns. This method consists of two essential contributions: a robust and automatic finger extraction method to isolate the fingers from the hand images; and a new feature extraction method based on an enhanced local line binary pattern (ELLBP). To overcome poorly imaged regions of the FTs, a method is suggested to salvage missing feature elements by exploiting the information embedded within the trained probabilistic neural network used to perform classification. Three databases have been applied in this study: PolyU3D2D, IIT Delhi and spectral 460 from Multi-spectral CASIA images. Experimental studies show that the best result was achieved by using ELLBP feature extraction. Furthermore, the salvaging approach proved effective in increasing the verification rate.

Gait is one of well-identified biometrics that has been broadly applied for human identification at a distance based on their motion style. However, the current gait recognition might have difficulties due to changing the viewing angles anduncertainty associated with gait signature extraction. This study deals with the design of an intelligent gait recognition system that tackles the problems mentioned above. This system is based on spatial-domain energy deviation image as a gait signature by adopting clustering technique to estimate the gait period in the gait sequence with arbitrary walking directions. To further improve the performance of the proposed system, interval type-2 fuzzy K-nearest neighbor classifier is used to diminish the effect of uncertainty formed by variations in gait signature extraction. Interval type-2 fuzzy set is involved in extending themembership values of each gait signature by using several initial K in order to handle and manage uncertainty that exists in choosing the initial value K. The proposed method realises the reduction in the dimensions of the gait feature and over-fitting. The comprehensive analyses reveal that the proposed algorithm can significantly enhance the multiple view gait recognition performance when being matched to the similar methods in the literature.

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingermarks and fingerprints were primarily developed for forensic investigation rather than for forensic evaluation purposes. Thus, in some of those algorithms, the computation of discriminating scores is speed-optimised. In the case of the AFIS algorithm used in this work, the speed-optimisation is achieved by performing the comparison in three different stages, each of which outputs scores of different magnitudes. As a consequence, all scores together present a multimodal distribution, even though each fingermark-to-fingerprint comparison generates one single score. This multimodal distribution of scores might be typical for other biometric systems or other algorithms, and the method proposed in this work can be also applied to those cases. As a result, the authors propose a probabilistic model for LR computation that presents more robustness to overfitting and data sparsity than other traditional approaches, like the use of models based on kernel density functions.

Lately, off-line signature verification systems have been reintroduced based on the idea of modelling the signature images with various relations among their pixels. In this paper, a modified version of the partially ordered set feature extraction procedure is presented by enabling distant range interactions between preceded relations of pixel groups. In this way, the spatial diversity of correlation that exists among signature features can be exploited. The motive behind this approach is related to our belief that the particular idiosyncratic writing style characteristics of each individual will be present over the whole length of the signature. Experiments involve the well-known Center of Excellence for Document Analysis and Recognition (CEDAR) and Ministerio de Ciencia y Tecnologia (MCYT) datasets in two popular writer dependent training modes: In the first mode, genuine and simulated forgeries were utilized, while in the second one genuine and random forgeries only were utilized during the training stage of the classifier. In both cases, the testing phase is exploited with genuine, simulated and random forgeries while receiver operating characteristic along with decision oriented FAR, FRR and average error metrics are assessing the proposed feature extraction method. The results obtained, show that the long range correlation of grid features can be efficiently employed for off-line signature verification.

Retinal vascular network pattern is unique to each individual which can be used for person identification in biometric authentication. In this study, the authors have proposed a novel biometric authentication method using retinal vascular branch, bifurcation and crossover points (i.e. feature points). The method automatically extracts the vascular network from colour retinal images and identifies these feature points. The major blood vessels characterised by width and length are identified from the segmented vascular network. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected as major vessels following an established protocol. The geometric hashing technique is developed to compute the invariant features from these feature points. They consider the feature points from major vessels which will be less susceptible to noise for modelling a basis pair and all other points together for locations in the hash table entries. The models are invariant to rotation, translation and scaling as inherited from geometric hashing. For each person, the system is trained with the models to accept or reject a claimed identity. They have tested their method on 3010 retinal images and achieved 96.64% precision and 100% recall.

This work presents a method based on information-theoretic analysis of iris biometric that aims to extract homogeneous regions of high entropy. Successful extraction of these regions facilitates the development of effective systems for generation of cryptographic keys of lengths up to 400 bits per iris. At the same time, this approach allows for the application of simpler error correction codes with equal false accept rate levels, which reduces the overall complexity of this class of systems.

The vascular patterns seen on the white of the eye, mainly in conjunctival and episcleral layers, are termed as ocular surface vasculature (OSV). OSV is visible in images captured with commercial RGB cameras, and its unique texture can be used for biometric recognition. This study demonstrates the capabilities of curvelet transform for OSV feature extraction. Non-linear feature enhancement and feature mapping in curvelet domain are shown to be effective in differentiating OSV texture. Linear discriminant analysis and similarity metrics are used for matching. A match-score level fusion is used across multiple gaze directions for both eyes. Using a multi-distance dataset of 50 volunteers, where eye images were acquired from 30, 150, and 250 cm using a dSLR, a best equal error rate (EER) of 0.2% is obtained. Using a second dataset of 40 volunteers acquired from 150 cm using a dSLR, a best EER of 3.1% is obtained. For a 216-participant dataset of ocular images acquired using cellular phones from close proximity, an EER of 0.9% is obtained. The proposed methodology was also tested on the publicly available UBIRIS V1 dataset, yielding an EER of 0.7%. The experimental results support the theoretically formulated advantages of the curvelet transform and its capability in successful extraction of curved structures when applied to OSV patterns.

In this study, a framework is proposed to super-resolve the long range captured iris polar images. In this study, modified diamond search and enhanced total variation algorithms are proposed to super-resolve the long range captured iris polar multi-frame images. The framework is tested on Chinese Academy of Sciences' Institute of Automation (CASIA) long range iris database by comparing and analysing the structural similarity index matrix, peak signal-to-noise ratio, visual information fidelity in pixel domain, and execution time of proposed algorithms with Yang and Nguyen state-of-the-art algorithms. The results demonstrate that the proposed framework is well suited for super-resolution of iris images captured at a long distance.

Biometrics such as retina, palm vein, wrist vein and hand vein are becoming increasingly popular because of ease of use, in-built liveness detection and a contact free capture process. Here the authors show the benefits of graph representation for all such vascular biometrics and the advantages of graph topology in matching vascular biometric graphs. The authors find that different types of small substructures dominate in the four classes of vascular graphs due to the way the vasculature is built in the human body. The authors show that simple graph structures can be used to bring down the registration time by over 50% compared with registration using edges alone, and that cost functions that include matching the local graph neighbourhood around compared node pairs improve matching performance. Distance measures based on the number of vertices or edges in the maximum common subgraph are shown to discriminate significantly better than using a simple count of matched vertices, as in the iterative closest point based point pattern matching. For graphs whose topology is close to that of proximity graphs, edge-based distance measures were found to perform best. These results are demonstrated for all four vascular modalities.

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classifiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature – there have been many good geometric features designed – but to smartly process the data there are at the authors’ disposal. This work proposes a gait recognition method without design of novel gait features; instead, the authors suggest an effective and highly efficient way of processing known types of features. Their method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classifier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. They experimentally demonstrate that their gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.