Hand Geometry Techniques: A Review

Volume 2 Issue 11 (November 2014

Finger Vein Image Enhancement Technique based on Gabor filter and Discrete Cosine Transform

Biometrics is a global technique to establish the identity of a person by measuring one of their physical or behavioral characteristics such as fingerprint, signature, iris, voice and face. Compared to these biometric techniques, the finger vein technique has distinct advantages as it helps to protect privacy and anonymity in automated individual recognition. Many studies showed that the finger vein images were of a low quality because of the variation in the tissues and uneven illumination. Hence, there is a need for effective image enhancement techniques, which can improve the quality of the images. In this study, we proposed a novel technique, which enhances the image quality of the finger veins. This method includes contrast amelioration, use of Gabor filters and image fusion, which generates an image with highly connective patterns. We used three criteria to evaluate the quality of processed images, the mean of grey values, the image entropy, and the image contrast. The obtained result shows higher values when using our approach in comparison to the baseline methods considered in this work

Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% whilemaintaining the classification accuracy

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

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

Advancing the technology of sclera recognition

PhD ThesisEmerging biometric traits have been suggested recently to overcome some challenges and issues related to utilising traditional human biometric traits such as the face, iris, and fingerprint. In particu- lar, iris recognition has achieved high accuracy rates under Near- InfraRed (NIR) spectrum and it is employed in many applications for security and identification purposes. However, as modern imaging devices operate in the visible spectrum capturing colour images, iris recognition has faced challenges when applied to coloured images especially with eye images which have a dark pigmentation. Other issues with iris recognition under NIR spectrum are the constraints on the capturing process resulting in failure-to-enrol, and degradation in system accuracy and performance. As a result, the research commu- nity investigated using other traits to support the iris biometric in the visible spectrum such as the sclera. The sclera which is commonly known as the white part of the eye includes a complex network of blood vessels and veins surrounding the eye. The vascular pattern within the sclera has different formations and layers providing powerful features for human identification. In addition, these blood vessels can be acquired in the visible spectrum and thus can be applied using ubiquitous camera-based devices. As a consequence, recent research has focused on developing sclera recog- nition. However, sclera recognition as any biometric system has issues and challenges which need to be addressed. These issues are mainly related to sclera segmentation, blood vessel enhancement, feature ex- traction, template registration, matching and decision methods. In addition, employing the sclera biometric in the wild where relaxed imaging constraints are utilised has introduced more challenges such as illumination variation, specular reflections, non-cooperative user capturing, sclera blocked region due to glasses and eyelashes, variation in capturing distance, multiple gaze directions, and eye rotation. The aim of this thesis is to address such sclera biometric challenges and highlight the potential of this trait. This also might inspire further research on tackling sclera recognition system issues. To overcome the vii above-mentioned issues and challenges, three major contributions are made which can be summarised as 1) designing an efficient sclera recognition system under constrained imaging conditions which in- clude new sclera segmentation, blood vessel enhancement, vascular binary network mapping and feature extraction, and template registra- tion techniques; 2) introducing a novel sclera recognition system under relaxed imaging constraints which exploits novel sclera segmentation, sclera template rotation alignment and distance scaling methods, and complex sclera features; 3) presenting solutions to tackle issues related to applying sclera recognition in a real-time application such as eye localisation, eye corner and gaze detection, together with a novel image quality metric. The evaluation of the proposed contributions is achieved using five databases having different properties representing various challenges and issues. These databases are the UBIRIS.v1, UBIRIS.v2, UTIRIS, MICHE, and an in-house database. The results in terms of segmen- tation accuracy, Equal Error Rate (EER), and processing time show significant improvement in the proposed systems compared to state- of-the-art methods.Ministry of Higher Education and Scientific Research in Iraq and the Iraqi Cultural Attach´e in Londo

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above