Online finger-knuckle-print verification for personal authentication

Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two datasets called Poly-U FKP and FKP are used for performance evaluation. The proposed method managed to achieve 94.91{%} and 98.5{%} recognition rates on the Poly-U FKP and FKP datasets and outperformed all of the existing methods in this respect. These results demonstrate the potential of this method as a simple yet effective solution for FKP-based identity recognition.

Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentication with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.

In universal, identification and authentication are done by smart cards, passwords which are gently broken by others. To conquer this event, biometrics has been induced as a only tool to cognize an individual person. The Biometric is a measure which comprises of distinctive natural characteristics that afford more authentication and safety than password, smart cards, etc. This literary has centered on the Finger Knuckle Print (FKP), and this paper proposes an innovative method to personal identification and identity authentication, using Gabor filter the fusion of PCA, LDA, MMDA algorithms with Euclidian distance measure. Moreover the fusion process helps to improve the recognition rate. For analyze the performance of the proposed method by utilization of Poly-U finger knuckle print database. The results of verification process are which shows efficiency and effectiveness of this proposed method.

1st International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics, ETCHB 2010, Istanbul, 22-22 August 2010

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

Finger Knuckle Print as Unimodal Fuzzy Vault Implementation

Multimodal biometric systems represent a significant advancement in biometric authentication technology by integrating multiple modalities to enhance accuracy and security. Our proposed system combines electrocardiogram (ECG), fingerprint, and finger knuckle print (FKP) modalities to achieve improved authentication performance, especially suited for high-security applications. The system first uses ECG for liveness detection, ensuring that only genuine users proceed, followed by fingerprint and FKP for authentication. Three individual Siamese Neural Networks were developed, each optimized to extract distinct features from each modality. Data quality was enhanced using preprocessing methods such as noise reduction and normalization, along with data augmentation strategies to improve model robustness. The system was evaluated using a balanced dataset of 50 samples per biometric modality. Individual average accuracies reached 99.54% for ECG, and 100% for both fingerprint and FKP considering 90 subjects. Using weighted average score-level fusion with a priority on fingerprint and FKP, the system achieved an overall accuracy of 99.80%, with a False Acceptance Rate (FAR) of 0.20%, False Rejection Rate (FRR) of 0.21%, Equal Error Rate (EER) of 0.20%, and an F1-Score of 99.80%. These results demonstrate the system's resilience to spoofing and robustness against data variability, offering a highly secure authentication solution with practical applicability in cloud-based high-security environments, such as banking and healthcare.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

A biometric authentication system provides an automatic person authentication based on some characteristic features possessed by the individual. Among all other biometrics, human retina is a secure and reliable source of person recognition as it is unique, universal, lies at the back of the eyeball and hence it is unforgeable. The process of authentication mainly includes pre-processing, feature extraction and then features matching and classification. Also authentication systems are mainly appointed in verification and identification mode according to the specific application. In this paper, preprocessing and image enhancement stages involve several steps to highlight interesting features in retinal images. The feature extraction stage is accomplished using a bank of Gabor filter with number of orientations and scales. Generalized Discriminant Analysis (GDA) technique has been used to reduce the size of feature vectors and enhance the performance of proposed algorithm. Finally, classification is accomplished using k-nearest neighbor (KNN) classifier to determine the identity of the genuine user or reject the forged one as the proposed method operates in identification mode. The main contribution in this paper is using Generalized Discriminant Analysis (GDA) technique to address ‘curse of dimensionality’ problem. GDA is a novel method used in the area of retina recognition.

This paper presents anovel method for texture classification and biometric authentication based on a descriptor called the weighted mean-based patterns (WEM). The proposed descriptor has been developed for extracting texture features from a large dataset of hand images, which has been created by the authors. The method uses the distinctive features of finger knuckle print (FKP) for hand image retrieval, which can be used in biometric identity recognition systems. The proposed method also includes a feature selection step for eliminating less important patterns and a weighted distance measure for quantifying the similarity of images. The method uses the support vector machine (SVM) for the classification stage. The proposed method has been tested on the FKP image dataset to evaluate the image retrieval performance, and also on Brodatz, Vistex, and Stex datasets to evaluate the performance for texture classification. Higher performance of the proposed method is demonstrated through comparison with other methods. The proposed method is shown to be sufficiently precise for a variety of applications, including identity recognition and classification.

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.

Person authentication using periocular images is a prominent research domain. Although the biometric identification systems have advanced, the existing approaches still struggle with accuracy, overfitting issues and computational efficiency, especially when utilizing periocular images for person identification and gender classification. In order to overcome these limitations, this paper proposes an enhanced deep learning classifier (EDLC) paradigm to recognize a person based on the periocular region within a face. A novel Hexagon-shaped ROI extraction is performed in the localization phase to extract the periocular ROIs. Following that, the feature extraction mechanism is accomplished utilizing the Laplacian transform. Finally, three distinct custom EDLCs are employed, such as dilated axial attention convolutional neural network, self-spectral attention-based relational transformer net, parameterized hypercomplex convolutional Siamese network for classification. Further, an adaptive coati optimization algorithm is used to adjust the hyperparameters of the classification models. The efficacy of the model is assessed concerning different outcome indicators. It is also compared with the recent competitive models. For person identification, the SSA-RTNet has achieved a maximum accuracy of 99.8% and 99.67% using the UBIPr and UFPR datasets respectively. Similarly, for gender classification, an accuracy of 98.4% and 99.68% using SSA-RTNet was obtained for the UBIPr and UFPR datasets. As a result, it is perceived that a considerable improvement was observed using the enhanced models.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention; yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084.

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.