Identification and Security Implications of Biometrics

The usage of biometrics has become more frequent over the past couple of decades, notably due to technological advancements. Evolving technology in the field of biometrics has also led to increased accuracy of associated software, which have provided the opportunity to use a multitude of different human characteristics for identification and/or verification purposes. The current study assessed the usage of biometrics in casinos, hospitals, and law enforcement agencies using a survey methodology. Results indicated that privacy concerns related to the use of biometrics may not be as prevalent as indicated in the literature. Additionally, results indicated that the utilization of biometrics has led to increased accuracy in identification and verification processes, led to enhanced security, and would be highly recommended to other institutions. Information obtained from the literature notes the racial bias in facial recognition technologies due to algorithmic development based solely upon features of Caucasian individuals. Efforts need to be made to create facial recognition algorithms that are more racially and ethnically diverse

Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition

Iris recognition algorithms, especially with the emergence of large-scale iris-based identification systems, must be tested for speed and accuracy and evaluated with a wide range of templates – large size, long-range, visible and different origins. This paper presents the acquisition of eye-iris images of dark-skinned subjects in Africa, a predominant case of verydark- brown iris images, under near-infrared illumination. The peculiarity of these iris images is highlighted from the histogram and normal probability distribution of their grayscale image entropy (GiE) values, in comparison to Asian and Caucasian iris images. The acquisition of eye-images for the African iris dataset is ongoing and will be made publiclyavailable as soon as it is sufficiently populated

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Global features with identical twins biometric identification system

Studies in pattern recognition domain currently revolve around twin’s biometric identification. The twins’ biometric Identification system may lead to the discovery of a distinguishing pattern of a biometric of an individual. A significant improvement can also be seen in the Unimodal biometric identification; it allows accurate and reliable identification of identical twins with good performance of certain traits. However, since the similarity level is very high, Identical twins’ identification is much more difficult when compared to that of non-twins. Hence, the use of more than one biometric trait with global features is proposed. Further, pattern recognition requires the extraction and selection of meaningful features, which leads to the key issue in the identification of twin handwriting-fingerprint, that is, the question of how to acquire features from many writing and styles twin handwriting-fingerprint to enable the reflection of the right person between twins. This study thus proposes the global with Aspect United Moment Invariant for global feature extractions with the application of identical twin multi-biometric identification with Inter-class and Intra-class

Twins multimodal biometric identification system with aspect united moment invariant

In the field of pattern recognition twin’s biometric identification is currently a popularly studied subject. In some situations, the mechanism of twins’ biometric Identification leads to the finding a distinctive pattern of a person’s biometric. Correspondingly, there has been considerable improvement made on the Unimodal biometric identification to identify identical twins with respect to its accuracy and reliability, with some traits that show sound performance. However, owing to great level of similarity, it is much more challenging to identify Identical twins as opposed to identifying non-twins. In order to deal with this problem, the application of more than one biometric trait is proposed; the Multimodal biometric system. Meanwhile, in pattern recognition it is crucial to extract and select features that are meaningful. This brings the attention to the major issue in twin handwriting-fingerprint identification: how to obtain features from numerous writing and styles twin handwriting-fingerprint so that the right person between twins can be reflected. Hence, the Aspect United Moment Invariant is proposed in this study as extraction of feature with identical twin multi-biometric identification

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

An enhanced iris recognition and authentication system using energy measure

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Using Personal Identification Number (PIN) or a password is no longer secure enough to identify an individual. Iris recognition is considered to be one of the best and accurate form of biometric measurements compared to others, it has become an interesting research area. Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an adopted Threshold Value. The information generated can further be used to find out different eye ailments. An iris is obtained using a predefined iris image which is scanned through eight (8) different stages and wavelet packet decomposition is used to generate 64 wavelet packages bit iris code so as to match the iris codes with Hamming distance criteria and evaluate different energy values. The system showed 98% True Acceptance Rate and 1% False Rejection Rate and this is because some of the irises weren’t properly captured during iris acquisition phase. The system is implemented using UBIRIS v.1 Database.Keywords: Local Image Properties, Authentication Enhancement, Iris Authentication, Local Image, Iris Recognition, Binary Bit Sequenc