Finger Knuckle Print Research Articles

Sparse representation of finger knuckle print images for personal identification

SummaryFraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.

Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Fusion of deep and local gradient-based features for multimodal finger knuckle print identification

Tensorized Multi-View Low-Rank Approximation Based Robust Hand-Print Recognition.

Since hand-print recognition, i.e., palmprint, finger-knuckle-print (FKP), and hand-vein, have significant superiority in user convenience and hygiene, it has attracted greater enthusiasm from researchers. Seeking to handle the long-standing interference factors, i.e., noise, rotation, shadow, in hand-print images, multi-view hand-print representation has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. However, the existing methods usually ignore the high-order correlations between different views or fuse very limited types of features. To tackle these issues, in this paper, we present a novel tensorized multi-view low-rank approximation based robust hand-print recognition method (TMLA_RHR), which can dexterously manipulate the multi-view hand-print features to produce a high-compact feature representation. To achieve this goal, we formulate TMLA_RHR by two key components, i.e., aligned structure regression loss and tensorized low-rank approximation, in a joint learning model. Specifically, we treat the low-rank representation matrices of different views as a tensor, which is regularized with a low-rank constraint. It models the across information between different views and reduces the redundancy of the learned sub-space representations. Experimental results on eight real-world hand-print databases prove the superiority of the proposed method in comparison with other state-of-the-art related works.

Joint multi-type feature learning for multi-modality FKP recognition

Multimodal biometric recognition has attracted intensive attention due to its significant performance improvement for personal authentication by exploiting multiple resources of data. However, most existing multimodal biometric recognition methods tend to fuse completely different biometric traits, making them hard to explore the complementary features of the multimodal data. In this paper, we propose a new multimodal biometric descriptor by jointly learning the multi-type collaborative features of multi-modality finger-knuckle-print (FKP) images. First, we form multi-type feature vectors to capture the texture and direction patterns of the multimodal FKP images. Then, we jointly learn the feature projection to map multi-type vectors into a compact FKP descriptor. Moreover, our method automatically selects the optimal weights for multi-type features during feature learning to make the learned feature codes discriminative. Lastly, we integrate the non-overlapping block-wise histograms of the learned binary codes as the final multimodal FKP feature descriptor. The experimental results conducted on the benchmark PolyU FKP database demonstrate the effectiveness of the proposed method for multimodal FKP recognition.

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

Retracted: Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features.

[This retracts the article DOI: 10.1155/2022/6041828.].

An Efficient Approach of Finger Knuckle Print Based Recognition Using Googlenet Model

Abstract: The need of personal recognition and identification became necessary in order to keep intruders away and to allow only the legitimate persons in order to protect privacy and to secure data from being altered. But, with the technological evolution and increasing risk of data theft, it has been a mandatory demand. A persons’s identity is not limited to his/her name, password or PIN but today, their biometrics are being used as an integral part of their identification. The emergence of Deep learning, a subset of Artificial Intelligence, brought a revolution in the field of computer vision and biometric identification. The Convolutional Neural Network (CNN) models of Deep learning possess a resemblance to the human brain and it brought a revolution in the field of computer vision and biometric identification. I adopted a transfer learning approach by using a pretrained CNN model, GoogleNetfor conducting my experiment. The GoogleNet model automatically does all the image processing operations as well as the extraction of features too. I chose a very unique human biometric trait for this experiment, which is a person’s Finger Knuckleprint (FKP). It bears a complex pattern and unique structures. I requested the knuckleprint samples from The Hong Kong Polytechnic University and I used its Contactless Finger Knuckle Images Database (Version 1.0). An excellent result obtained by conducting this experiment. Keyword: Finger Knuckle Print, Convolution, GoogleNet, Feature Extraction, Classifier.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system

Biometric recognition systems are widely being used in several applications due to its distinctiveness and reliability. In recent years, hand-based person recognition has received much momentum due to its stability, feature richness, reliability and higher user acceptability. In this paper, we propose a multimodal hand biometric system based on Finger Knuckle Print (FKP) and Palmprint. In particular, the PCANet deep learning method is employed to extract distinctive features from each modality. Then, multiclass SVM is utilized to compute a matching score for each individual modality. Finally, score level fusion is performed to combine the matching scores via different rules such as, min, sum, max and multiplication. The performance of the proposed system is evaluated on the publicly available database known as PolyU. First, we conducted several experiments on single FKP and Palmprint traits. Next, score level fusion based multimodal experiments were performed. The proposed framework was able to achieve 0.00% of EER (equal error rates) and 100% of rank-1 performance. In addition, the proposed system based on PCANet with score level fusion of FKP and Palmprint outperformed existing multimodal methods.

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features.

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users' satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition

Automated biometric authentication attracts the attention of researchers to work on hand-based images to develop applications in forensics science. Finger Knuckle Print (FKP) is one of the hand-based biometrics used in the recognition of an individual. FKP is rich in texture, less in contact and known for its unique features. The dimensionality of the features, extracted from the image, is one of the main problems in pattern recognition. Since selecting the relevant features is an important but challenging task, the feature subset selection is an optimization problem. A reduced number of features results in enhanced classification accuracy. The proposed FKP system presents a mulitalgorithm fusion based on subspace algorithms at feature level fusion technique. In this paper, a new feature-selection algorithm, which is a Modified Magnetotatic bacterium Optimization Algorithm (MMBOA), is proposed for finger knuckle recognition to select relevant and useful features that increase the classification accuracy. The distinct characteristic of this bacterium influences the design of a new optimization technique. The hybrid features such as Eigen and Fisher (EiFi) are extracted from the finger knuckle. The fusion of this feature vector is optimized using newly proposed MMBOA_mr optimization algorithm. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms with a recognition accuracy of 99.7% with 22 features with the reduction rate of 72%. Additionally, the proposed approach is compared with the state-of-the-art methods.

Multi-instance Finger Knuckle Print Recognition based on Fusion of Local Features

Personal identity has become an important asset in today's digital world for any individual in society. Biometrics offers itself as a reliable and secure guarantor of our identities, so it has become essential to build efficient and robust recognition systems. In this orientation, we propose a fusion approach, which aims to optimally exploit the dividing block dimensions in the case of local methods to reduce similarities. We will use the compound local binary model (CLBP) for local features extraction, a robust operator descriptor that exploits both the sign and the inclination information of the differences between the center and the neighbor gray values. The reliability of the proposed approach was evaluated on the PolyU Finger Knuckle Print (FKP) database. We presented several experimental results that show the detailed path of our approach, explain the choices made for each step and illustrate the significant improvements compared to other existing recognition systems in the literature. The recognition rate of the proposed global approach is one of the highest among the other methods. Optimal final approach recognition rates vary between 99.70% and 100%.

FKPIndexNet: An efficient learning framework for finger-knuckle-print database indexing to boost identification

This paper addresses the problem of identification in the Finger-knuckle-print (FKP) databases. Identification determines the identity of a query of the FKP sample. It involves finding the most similar sample in the database by comparing the query FKP with all the templates stored in the database. It is a computationally expensive process that demands huge time for large databases. A technique is required that can reduce the search space and limits the number of comparisons to boost the identification process. Such a technique is called indexing. It devises a fixed size small candidate list for a given FKP sample in constant time for searching. The paper proposes FKPIndexNet that learns similarity-preserving hash codes for generating an index table. It employs a specialized autoencoder network to learn feature embeddings such that they have high intra-class and low inter-class similarity. The proposed technique is examined on two publicly available FKP databases viz., PolyU-FKP and IITD-FKP. Experimental results show that the proposed method achieves 100% hit rate at a penetration rate of only 3.42% for PolyU-FKP database and 0.32% for IITD FKP database, respectively. This implies that for a query FKP sample, to get a true match with 100% confidence, only 3.42% and 0.32% of the PolyU-FKP and IITD FKP database needs to be compared, respectively. Results and analysis demonstrate the superiority of the proposed technique compared to other state-of-the-art approaches.

Biometric authentication using a deep learning approach based on different level fusion of finger knuckle print and fingernail

This paper presents a deep learning method for human authentication based on hand dorsal characteristics. The proposed method uses the fingernail (FN) and the finger knuckle print (FKP) extracted from the ring, middle and index fingers. The proposed method was evaluated using a dataset of 1090 hand dorsal images (10 each from 109 persons) which are processed by the hand skin detection, the denoising method, and the procedure adopted for extraction of both finger knuckle and fingernail. A multimodal biometric scheme is used to improve the authentication performance of the proposed system and make it more resistant to spoofing attacks. A Deep learning-based approach using a convolutional neural network (CNN) with AlexNet as a pre-trained model is employed. Different features, extracted from hand images, were combined at different levels using normalization and fusion methods proposed by the authors. Experimental results demonstrate efficiency, robustness, and reliability of the proposed biometric system compared to existing alternatives. Consequently, it can be developed in many real-world applications.

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Finger-Knuckle-Print (FKP) is an accurate and reliable biometric in compare to other hand-based biometrics like fingerprint because of the finger's dorsal region is not exposed to surfaces. In this paper, a simple end-to-end method based on Convolutional Neural Network (CNN) is proposed for FKP recognition. The proposed model is composed only of three convolutional layers and two fully connected layers. The number of trainable parameters hereby has significantly reduced. Additionally, a straightforward method is utilized for data augmentation in this paper. The performance of the proposed network is evaluated on Poly-U FKP dataset based on 10-fold cross-validation. The best recognition accuracy, mean accuracy and standard deviation are 99.83%, 99.18%, and 0.76, respectively. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of recognition accuracy and the number of trainable parameters. Also, in compare to four fine-tuned CNN models including AlexNet, VGG16, ResNet34, and GoogleNet, the proposed simple method achieved higher performance in terms of recognition accuracy and the numbers of trainable parameters and training time.

Finger Knuckle Print based Biometric System Creation using Dual Clustering with Radial Basis-Manhattan Length Approach

Finger Knuckle Print based Biometric System Creation using Dual Clustering with Radial Basis-Manhattan Length Approach

A Biometric System Design using Finger Knuckle Biological Trait

Various biometric traits are available but recently finger knuckle image has attracted great attention to biometric research community due to its potentiality and ease of use. The performance of any biometric system heavily depends on the accuracy of feature detection and robustness of feature description. A number of feature descriptors are available but selection is being determined by the type of application such as image retrieval, biometric system, remote sensing etc. This paper proposed a biometric system using leading descriptor for finger knuckle biological trait image recognition and also compare the proposed system with existing leading state-of-art finger knuckle print recognition. The recognition performance is measured by some standard evaluation protocol such as Equal Error Rate (EER), Decidability index, Computation cost, Zero-score imposter probability, Zero-score genuine probability, Receiver Operating Characteristic (ROC), Detection Error Trade-off (DET) over PolyU Finger Knuckle benchmark database. The experimental results show that, the performance of SURF, KAZE and ORB are comparable and are better as compared to BRISK and MSER descriptor. The ERR of 0.0010% is obtained with ORB descriptor while the Decidability index of 6.4645 is obtained for KAZE. The minimum Computational cost of 0.1442 s is obtained for SURF as compares to other of its class.

Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework

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.

New Weighted Mean-Based Patterns for Texture Analysis and Classification

ABSTRACT 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.