Fingerprint Fusion Research Articles

A cancelable multi-biometric system based on the feature-level fusion of fingerprint and finger vein

A cancelable multi-biometric system based on the feature-level fusion of fingerprint and finger vein

Feature Level Fusion of Fingerprints and IRIS for the Enhancement of Biometric System

Background:: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results:: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion:: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

Review of iris segmentation and recognition using deep learning to improve biometric application

Abstract Biometric recognition is essential for identifying people in security, surveillance, and mobile device authentication. Iris recognition (IR) biometrics is exact because it uses unique iris patterns to identify individuals. Iris segmentation, which isolates the iris from the rest of the ocular image, determines iris identification accuracy. The main problem is concerned with selecting the best deep learning (DL) algorithm to classify and estimate biometric iris biometric iris. This study proposed a comprehensive review of DL-based methods to improve biometric iris segmentation and recognition. It also evaluates reliability, specificity, memory, and F-score. It was reviewed with iris image analysis, edge detection, and classification literature. DL improves iris segmentation and identification in biometric authentication, especially when combined with additional biometric modalities like fingerprint fusion. Besides, that DL in iris detection requires large training datasets and is challenging to use with noisy or low-quality photos. In addition, it examines DL for iris segmentation and identification efforts to improve biometric application understanding. It also suggests ways to improve precision and reliability. DL may be used in biometric identification; however, further study is needed to overcome current limits and improve IR processes.

Quality-Aware Fusion of Multisource Internal and External Fingerprints Under Multisensor Acquisition

Fingerprint is one of the most widely used biometric features. The researches on external fingerprint collected by total internal reflection and the internal fingerprint obtained by optical coherence tomography have been carried out extensively. Studies proved the consistency between them. The external fingerprint is susceptible to the fingertip status, whereas the internal fingerprint has strong anti-interference and anti-spoofing capabilities. They originate from different skin layers and are collected by multiple sensors. Given the corresponding advantages and disadvantages of external and internal fingerprints, their fusion can maximize effective information and improve fingerprint image quality; thus, this fusion is conducive to fingerprint identification. In this study, a fingerprint fusion method based on the quality-aware convolutional-sparsity-based morphological component analysis (CSMCA) is proposed. The proposed method realizes the fusion of multisource and multisensor fingerprints for the first time. Quality indexes, namely, spatial consistency, dryness, and humidity, are selected. Moreover, a simulation-based combination scheme is proposed for the pixel-level quality representation. The quality index is integrated with CSMCA for quality-aware fusion, which retains high-quality components and reduces low-quality areas. The experiments prove the superiority of the proposed method in quality scores and matching performances, indicating that internal fingerprints can amend external fingerprints. The matching experiments with either external or internal fingerprints show that the fused fingerprints can be compatible with the existing fingerprint databases. Our work can provide references and insights into identifying mutilated fingerprints in the future.

Fingerprint Fusion Location Method Based on Wireless Signal Distribution Characteristic

In the context of the rapid development of the Internet and the Internet of Things technology, services based on location information have received more and more attention, and people gradually have higher expectations for the quality and experience of positioning services. At present, outdoor positioning technology is becoming mature, but different from empty outdoor areas, there is a highly complex indoor environment with many interference factors, so it is difficult to receive effective satellite signals. To realize the smooth transition of whole-field positioning, it is necessary to study an economical and efficient indoor positioning technology. The existing indoor positioning technologies have some problems, so this paper comprehensively uses the resource-rich Wi-Fi signal, Frequency Modulation (FM) signal and Digital Terrestrial Multimedia Broadcast (DTMB) signal as the positioning data sources, and proposes a fingerprint fusion positioning method based on the wireless signal distribution characteristic. Experiments show that the proposed method improves localization accuracy by 30% compared to localization with Wi-Fi alone.

Fingerprinting-Based Indoor Positioning Using Data Fusion of Different Radiocommunication-Based Technologies

Wireless-radio-communication-based devices are used in more and more places with the spread of Industry 4.0. Localization plays a crucial part in many of these applications. In this paper, a novel radiocommunication-based indoor positioning method is proposed, which applies the fusion of fingerprints extracted with various technologies to improve the overall efficiency. The aim of the research is to apply the differences, which occur due to that different technologies behave differently in an indoor space. The proposed method was validated using training and test data collected in a laboratory. Four different technologies, namely WiFi received signal strength indication (RSSI), ultra-wideband (UWB) RSSI, UWB time of flight (TOF) and RSSI in 433 MHz frequency band and all of their possible combinations, were tested to examine the performance of the proposed method. Three widely used fingerprinting algorithms, the weighted k-nearest neighbor, the random forest, and the artificial neural network were implemented to evaluate their efficiency with the proposed method. The achieved results show that the accuracy of the localization can be improved by combining different technologies. The combination of the two low-cost technologies, i.e., the WiFi and the 433 MHz technology, resulted in an 11% improvement compared to the more accurate technology, i.e., the 433 MHz technology. Combining the UWB module with other technologies results in a less significant improvement since this sensor provides lower error rates, when used alone.

Short video fingerprint extraction: from audio–visual fingerprint fusion to multi-index hashing

Short video fingerprint extraction: from audio–visual fingerprint fusion to multi-index hashing

Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

WiFi fingerprint based indoor localization has become a key research direction in the field of indoor localization due to its high positioning accuracy and low equipment deployment cost. Increasing the number of reference point collected offline can improve the positioning accuracy, however it yields excessive cost of offline collection. Fingerprint augment is an effective solution to reduce the cost while ensuring the positioning accuracy. In this paper, we are pioneering to propose a fingerprint augment framework based on super-resolution (FASR), which achieves the fusion of fingerprint augment and super-resolution based on mutual conversion between fingerprint data and fingerprint image. The processing framework of FASR is formulated and the implementation of three modules in FASR are given, including Fingerprint-To-Image Conversion module, Super-Resolution module and Image-To-Fingerprint Conversion module. Simulated and real data experiments reveal the feasibility and effectiveness of the FASR. In addition, we explore the impact of two key engineering parameters on the performance of the FASR method. Our work demonstrates the new application of super-resolution in image processing field in wireless indoor localization topics.

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

A multilayer system to boost the robustness of fingerprint authentication against presentation attacks by fusion with heart-signal

Vulnerability to presentation attacks (PAs) remains one of the main security concerns of the widely used fingerprint-based authentication systems, especially for unattended and remote applications. PAs can be carried out by presenting artifact, corpse, or conformant samples to a biometric sensor with the intention of circumventing the system policy. In this study, we develop a multilayer biometric authentication system robust against PAs by the fusion of fingerprint and heart-signal. In the first layer, artifact attacks are prevented by using a fine-tuned convolutional neural network (CNN). In the second layer, a lightweight CNN is used for the prevention of corpse attacks by using heart-signal (also known as ECG signal) with duration of 0.5 s. In the subsequent layers, robust fingerprint matcher at a specific threshold are utilized for the prevention of conformant attacks. In the final layer, a score-level fusion of the fingerprint and heart-signal is used for biometric authentication. The proposed system was evaluated by different authentication and attack scenarios using a multimodal dataset comprising two public databases of fingerprints and heart-signals available online. The experimental results yielded a false match rate (FMR) of approximately zero (0.1%) with an acceptable false non-match rate (FNMR). The obtained results are encouraging for the incorporation of the system into applications requiring high-security authentication.

Secure Multimodal Authentication Scheme for Wireless Sensor Networks

In the current era, it is necessary to device authorization and authentication techniques to secure resources in information technology. There are several methods to substantiate authorization and authentication. User authentication is essential for authenticating user access control in WSNs. Biometric recognition error, lack of anonymity and vulnerability to attacks, user verification problem, revocation problem and disclosure of session key by the gateway node are some of the security flaws encountered. In this study, a Multimodal Authentication Scheme for Wireless Sensor Networks (WSN-MAS) is proposed to authenticate legitimate users. The main objective is the fusion of fingerprint and iris biometric features at feature level to enable additional accuracy to verify and match user identity with stored templates. In this paper, multimodal biometric features are used for authentication to improve performance, reduce system error rates to achieve better security in WSN.

Fingerprint Intramodal Biometric System Based on ABC Feature Fusion

Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.

A multibiometric system based on the fusion of fingerprint, finger-vein, and finger-knuckle-print

Authentication systems play an essential role in our lives today. Human biological, behavioral, and morphological characteristics are usually used in an authentication system in a vast scope of applications ranging from unlocking consumer devices to surveillance and forensic analysis and offered an alternative to credit cards, ID cards, passports, driving licenses, etc. Unibiometric systems, which use a single biometric modality, suffer from some drawbacks such as low protection of user’s privacy against attacks. Multibiometric systems, which fuse features of biometric characteristics, can cope with unibiometric systems’ drawbacks and improve security and recognition accuracy. However, some main questions such as ‘what is the optimal number of biometric modalities?’, ‘how much accuracy do we need for our system?’, and ‘how much money do we want to invest for our system?’ have to be considered and answered before designing and implementing a multibiometric system. Unfortunately, the existing multibiometric systems have not considered and responded to all questions. Furthermore, identification mode for multibiometric systems is a challenging task and almost all of them have focused on verification mode. In this paper, we only consider a finger and employ the maximum possible modalities of the finger, i.e., fingerprint, finger-vein, and finger-knuckle-print, to increase user-friendliness and reducing the cost of implementation of the system. The proposed system only uses three cameras to capture all contactless fingerprint, finger-vein, and finger-knuckle images. Also, we propose an algorithm that makes enable the system to work in identification mode. The experiments on established databases exhibit that our proposed algorithm significantly increases recognition accuracy.

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

A New Approach to External and Internal Fingerprint Registration With Multisensor Difference Minimization

External fingerprints captured by conventional scanners are easily affected by pollution or abrasion. Optical coherence tomography can capture stable internal fingerprints located inside fingertips but with comparable unstable qualities. External and internal fingerprints have advantages and disadvantages that can be used to complement each other. Registration is the premise of the fusion of these fingerprints. Multisensor differences between external and internal fingerprints, including image style differences and deformations, make the registration challenging. This paper presents a new multisensor fingerprint registration method to align external and internal fingerprints finely for the first time. The proposed method includes two stages. First, a cycle generative adversarial network with a structural constraint is designed to unify the image style of multisensor fingerprints. It can improve the similarity of fingerprints while preserve the valley–ridge structures. Then, a two-step registration with fast digital image correlation and outlier rejection is proposed to realize global to local registration. Comparisons have been performed to demonstrate the advantage of the proposed method in registration accuracy and matching performance. Fingerprint fusion results also verify that the registration and fusion of external and internal fingerprints are conducive to supplement fingerprint information.

Analyzing the Effects of Data Augmentation on Single and Multimodal Biometrics

Now-a-days, in the field of machine learning the data augmentation techniques are common in use, especially with deep neural networks, where a large amount of data is required to train the network. The effectiveness of the data augmentation technique has been analyzed for many applications; however, it has not been analyzed separately for the multimodal biometrics. This research analyzes the effects of data augmentation on single biometric data and multimodal biometric data. In this research, the features from two biometric modalities: fingerprint and signature, have been fused together at the feature level. The primary motivation for fusing biometric data at feature level is to secure the privacy of the user’s biometric data. The results that have been achieved by using data augmentation are presented in this research. The experimental results for the fingerprint recognition, signature recognition and the feature-level fusion of fingerprint with signature have been presented separately. The results show that the accuracy of the training classifier can be enhanced with data augmentation techniques when the size of real data samples is insufficient. This research study explores that how the effectiveness of data augmentation gradually increases with the number of templates for the fused biometric data by making the number of templates double each time until the classifier achieved the accuracy of 99%.

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection.

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images.

In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

Aiming at the problems of high time overhead, low positioning accuracy, and inability to meet the requirements of indoor positioning applications in WiFi and Pedestrian Dead Rockoning (PDR) positioning technologies, a cross-layer positioning method based on multi-sensor and WiFi fingerprint fusion is proposed. Firstly, Multi-dimensional scaling technology (MDS) is used in WiFi location, which reduces the overhead of offline stage of large area WiFi fingerprint location and improves the responsiveness of online location. Without limiting the holding mode, the high and low threshold detection method is used to reduce the error in gait detection of the PDR method. Unscented Kalman Filter (UKF) is used to fusion WiFi and PDR positioning results, output the optimal two-dimensional estimation, and finally use WiFi signal and multi-sensor information to determine the height of pedestrian position and output three-dimensional coordinates. The experiment results show that the proposed method have higher accuracy and better stability compared with WiFi and PDR positioning method.