Finger Knuckle Patterns Research Articles

The robust SmartEnsembleNet: A game changer in finger knuckle biometrics

SmartEnsembleNet represents a major leap forward in the domain of biometric identification, focusing on the relatively underexplored area of finger knuckle biometrics. This research enhances the effectiveness and uniqueness of biometric systems by leveraging the distinct and secure features of finger knuckle patterns. As a complement to existing biometric techniques like fingerprints and facial recognition, it adds an additional layer of security and reliability. Addressing the challenges faced by finger knuckle identification, such as image acquisition, environmental effects, and aging, SmartEnsembleNet introduces a pioneering multi-layered ensemble learning strategy that combines eight diverse machine learning models. These models, trained via a 5-fold cross-validation on datasets from IIT Delhi and PolyU, demonstrate significant improvements in accuracy, precision, recall, and F1-score. A distinctive feature of SmartEnsembleNet is its innovative meta-model, streamlining the creation of a meta-dataset. This dataset is created by applying bilinear interpolation to the predicted probabilities from the top-performing model, and it is further refined by subtracting these interpolated probabilities from the validation data. This iterative process generates ‘smart data’, which serves as the basis for training the ultimate ‘smart model’. Logistic regression is chosen for its outstanding performance. This method, applied uniformly to both training and testing phases, considerably enhances classification accuracy in finger knuckle biometrics, establishing new benchmarks. SmartEnsembleNet’s performance undergoes a detailed evaluation using the Friedman test and Nemenyi post-hoc analysis, guaranteeing a selection process grounded in statistical rigor. With its resilience to noise and adaptability under various conditions, SmartEnsembleNet not only addresses existing gaps in finger knuckle biometric research but also pioneers future advancements, providing a versatile and secure solution impactful for both academia and high-security scenarios.

Completely Contactless and Online Finger Knuckle Identification for Real World Applications

Completely contactless and at-a-distance personal identification provides enhanced user convenience, and improved hygiene and is highly sought under the COVID-19 pandemic. This paper proposes an accurate and generalizable deep neural network-based framework for the ‘completely’ contactless finger knuckle identification. We design and introduce a new loss function to enable a fully convolutional network to more effectively learn knuckle features that are imaged under at-a-distance imaging. A ‘completely’ contactless system also requires efficient online finger knuckle detection capabilities. This paper, for the first time in our knowledge, develops and introduces accurate capabilities to efficiently detect and segment finger knuckle patterns from images with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complex</i> backgrounds as widely observed in real-world applications. We introduce angular loss to accurately predict oriented knuckle patterns and incorporate into our framework. Experimental results presented in this paper on five different public databases, using challenging protocols and cross-database performance evaluation, illustrate outperforming results and validate the effectiveness of the proposed framework for completely contactless applications.

CNN-Based Transfer Learning for 3D Knuckle Recognition

A trustworthy and secure identity verification system is in great demand nowadays. The automatic recognition of the 3D middle finger knuckle is a new biometric identifier that could offer a precise, practical, and efficient alternative for personal identification. According to earlier studies, deep learning algorithms could be used for biometric identification. However, the accuracy of the current 3D middle finger knuckle recognition model is relatively low. Motivated by this fact, in this study, seven deep learning neural networks have been modified and trained to identify 3D middle finger knuckle patterns using transfer learning. Using the Hong Kong Polytechnic University’s 3D knuckle image dataset, an extensive experiment was performed. Two sessions of data from different camera lenses were used to assess the performance of the suggested deep learning model. The results show that the InceptionV3 method significantly enhanced the recognition of 3D middle finger knuckle patterns with 99.07% accuracy, followed by Xception, NasNetMobile, and DenseNet201 (97.35%, 92.92%, and 92.59%, respectively), which is superior to the current middle finger knuckle recognition model. This accurate, fast, and automatic middle finger knuckle identification will help to be implemented in real-time and small-scale settings like offices, schools, or personal devices like laptops and smartphones, where training is simple.

Score level fusion of major and minor finger knuckle patterns based symmetric sum-based rules for person authentication

Score level fusion of major and minor finger knuckle patterns based symmetric sum-based rules for person authentication

Contactless person recognition using 2D and 3D finger knuckle patterns

Contactless person recognition using 2D and 3D finger knuckle patterns

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

Contactless Biometric Identification Using 3D Finger Knuckle Patterns.

Study on finger knuckle patterns has attracted increasing attention for the automated biometric identification. However, finger knuckle pattern is essentially a 3D biometric identifier and the usage or availability of only 2D finger knuckle databases in the literature is the key limitation to avail full potential from this biometric identifier. This paper therefore introduces (first) contactless 3D finger knuckle database in public domain, which is acquired from 130 different subjects in two-session imaging using photometric stereo approach. This paper investigates on the 3D information from the finger knuckle patterns and introduces a new feature descriptor to extract discriminative 3D features for more accurate 3D finger knuckle matching. An individuality model for the proposed feature descriptor is also presented. Comparative experimental results using the state-of-the-art feature extraction methods on this challenging 3D finger knuckle database validate the effectiveness of our approach. Although our feature descriptor is designed for 3D finger knuckle patterns, it is also attractive for other hand-based biometric identifiers with similar patterns such as the palmprint and fingerprint. This observation is validated from the outperforming results, using the state-of-the-art pixel-wise 3D palmprint and 3D fingerprint feature descriptors, on other publicly available datasets.

Can finger knuckle patterns help strengthen the e-banking security

Communication via the internet has become vital for any kind of information exchange private, public, commercial, or military. Banks are the first that have used the internet for financial transactions (e-banking). However, the safe use of e-banking implies that all precautions have been considered to identify legitimate users and thus avoid economic and social damage that may be caused by any possible fraud. In this context, we propose in this paper a secure biometric system dedicated to e-banking for reducing the fraud risk and strengthening the customer confidence. The fuzzy commitment concept associated with the finger-knuckle-print (FKP) is the core of our proposed system. However, such a system will only be efficient if the FKP features are accurately extracted. For this, we have developed a new method of feature extraction called adaptive extended binary pattern (AELBP). The obtained experimental results have been judged promising for a high security of e-banking with guaranteed trust from costumers.

Can finger knuckle patterns help strengthen the e-banking security

Communication via the internet has become vital for any kind of information exchange private, public, commercial, or military. Banks are the first that have used the internet for financial transactions (e-banking). However, the safe use of e-banking implies that all precautions have been considered to identify legitimate users and thus avoid economic and social damage that may be caused by any possible fraud. In this context, we propose in this paper a secure biometric system dedicated to e-banking for reducing the fraud risk and strengthening the customer confidence. The fuzzy commitment concept associated with the finger-knuckle-print (FKP) is the core of our proposed system. However, such a system will only be efficient if the FKP features are accurately extracted. For this, we have developed a new method of feature extraction called adaptive extended binary pattern (AELBP). The obtained experimental results have been judged promising for a high security of e-banking with guaranteed trust from costumers.

Finger kunckcle patterns based person recognition via bank of multi-scale binarized statistical texture features

This paper proposes a novel finger knuckle patterns (FKP) based biometric recognition system that utilizes multi-scale bank of binarized statistical image features (B-BSIF) due to their improved expressive power. The proposed system learns a set of convolution filters to form different BSIF feature representations. Later, the learnt filters are applied on each FKP traits to determine the top performing BSIF features and respective filters are used to create a bank of features named B-BSIF. In particular, the presented framework, in the first step, extracts the region of interest (ROI) from FKP images. In the second step, the B-BSIF coding method is applied on ROIs to obtain enhanced multi-scale BSIF features characterized by top performing convolution filters. The extracted feature histograms are concatenated in the third step to produce a large feature vector. Then, a dimensionality reduction procedure, based on principal component analysis and linear discriminant analysis techniques (PCA + LDA), is carried out to attain compact feature representation. Finally, nearest neighbor classifier based on the cosine Mahalanobis distance is used to ascertain the identity of the person. Experiments with the publicly available PolyU FKP dataset show that the presented framework outperforms previously-proposed methods and is also able to attain very high accuracy both in identification and verification modes.

A practical person authentication system using second minor finger knuckles for door security

This paper proposes a person authentication system using second minor finger knuckles, i.e., metacarpophalangeal (MCP) joints, for door security. This system acquires finger knuckle patterns on MCP joints when a user takes hold of a door handle and recognizes a person using MCP joint patterns. The proposed system can be constructed by attaching a camera onto a door handle to capture MCP joints. Region of interest (ROI) images around each MCP joint can be extracted from only one still image, since all the MCP joints are located on the front face of the camera. Phase-based correspondence matching is used to calculate matching scores between ROIs to take into consideration deformation of ROIs caused by hand pose changes. Through a set of experiments, we demonstrate that the proposed system exhibits the efficient performance of MCP recognition and also show the potential possibilities of second minor finger knuckles for biometric recognition.

Personal Identification Using Minor Knuckle Patterns From Palm Dorsal Surface

Finger or palm dorsal surface is inherently revealed while presenting (slap) fingerprints during border crossings or during day-to-day activities, such as driving, holding arms, signing documents, or playing sports. Finger knuckle patterns are believed to be correlated with the anatomy of fingers that involve complex interaction of finger bones, tissues, and skin, which can be uniquely identify the individuals. This paper investigates the possibility of using lowest finger knuckle patterns formed on joints between the metacarpal and proximal phalanx bones for the automated personal identification. We automatically segment such region of interest from the palm dorsal images and normalize/enhance them to accommodate illumination, scale, and pose variations resulting from the contactless imaging. The normalized knuckle images are investigated for the matching performance using several spatial and spectral domain approaches. We use database of 501 different subjects acquired from the contactless hand imaging to ascertain the performance. This paper also evaluates the possibility of using palm dorsal surface regions, in combination with minor knuckle patterns, and provides finger dorsal image database from 712 different subjects for the performance evaluation. The experimental results presented in this paper are very encouraging and demonstrates the potential of such unexplored minor finger knuckle patterns for the biometrics identification.

Personal recognition using finger knuckle shape oriented features and texture analysis

Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

Recovering and matching minutiae patterns from finger knuckle images

Personal identification approaches using finger knuckle patterns are receiving increasing attention in the biometrics literature. Several approaches have been explored and these methods consider knuckle patterns as textured-like patterns, similar to the iris, and illustrate promising results. However much of the law-enforcement and forensic analysis for hand biometrics still relies on recovery and matching of minutiae patterns which has matured in last several decades. This is largely due to the fact that the identification of minutiae patterns is believed to be more scientific and pervasive in connecting with the anatomy/uniqueness of individuals. Availability of high resolution finger dorsal images acquired for recreational or covert imaging can provide important cues for forensic investigation and analysis, especially when finger dorsal patterns are only the piece of evidence available for the identification. Identification of finger knuckle patterns using minutiae recovery and matching is expected to significantly help in prosecution of such suspects. This paper therefore investigates recovery and matching of minutiae patterns using finger knuckle images. We investigate effective use of minutiae quality in improving performance for the knuckle pattern matching. Our study detailed in this paper also presents comparative evaluation of performance using three popular minutiae matching approaches. The experimental results presented in this paper from a database from 120 different subjects are highly encouraging and validate such first attempt to study minutiae recovery and matching from finger knuckle images.

Importance of Being Unique From Finger Dorsal Patterns: Exploring Minor Finger Knuckle Patterns in Verifying Human Identities

Automated biometrics identification using finger knuckle images has increasingly generated interest among researchers with emerging applications in human forensics and biometrics. Prior efforts in the biometrics literature have only investigated the major finger knuckle patterns that are formed on the finger surface joining proximal phalanx and middle phalanx bones. This paper investigates the possible use of minor finger knuckle patterns, which are formed on the finger surface joining distal phalanx and middle phalanx bones. The minor finger knuckle patterns can either be used as independent biometric patterns or employed to improve the performance from the major finger knuckle patterns. A completely automated approach for the minor finger knuckle identification is developed with key steps for region of interest segmentation, image normalization, enhancement, and robust matching to accommodate image variations. This paper also introduces a new or first publicly available database for minor (also major) finger knuckle images from 503 different subjects. The efforts to develop an automated minor finger knuckle pattern matching scheme achieve promising results and illustrate its simultaneous use to significantly improve the performance over the conventional finger knuckle identification. Several open questions on the stability and uniqueness of finger knuckle patterns should be addressed before knuckle pattern/image evidence can be admissible as supportive evidence in a court of law. Therefore, this paper also presents a study on the stability of finger knuckle patterns from images acquired with an interval of 4–7 years. The experimental results and the images presented in this paper provide new insights on the finger knuckle pattern and identify the need for further work to exploit finger knuckle patterns in forensics and biometrics applications.