Multimodal Biometric Feature Research Articles

Blockchain and FEF-Based Lightweight Anonymous Authentication Protocol for Wireless Medical Sensor Networks

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management

Medical care conveyance has been transformed by the Internet of Things (IoT's) combination into wellbeing systems, which provides doctors and patients with continuous on-request services. However, this coordination poses questions with respect to the precision of the information and possible security risks. This research expects to present a sharp character the executives structure planned for IoT and distributed computing based personalized medical care frameworks. The purpose is to upgrade confirmation processes while restricting security threats through the double-dealing of multimodal encoded biometric features. The suggested approach incorporates biometric-based continuous authentication together with combined and concentrated personality access strategies. To safeguard patient information in the cloud, it combines electrocardiogram (ECG) and photoplethysmogram (PPG) signals for authentication, which is further bolstered by homomorphic encryption (HE). An AI (ML) model was used to assess the system's reasonability including a dataset of 20 clients in various seating configurations. The merged based biometric structure defeated standalone ECG or PPG signal-based procedures in perceiving and authenticating every client with 100% exactness. The proposed framework makes significant improvements to the privacy and security of personalized healthcare frameworks. It fulfills the essential security necessities and is by the by viable enough to run on low-end processors. It guarantees trustworthy authentication and protects against conventional security threats by utilizing multimodal biometric features and cutting-edge encryption techniques.

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from different biometric images that includes iris, palm print, and lip, where the images are preprocessed initially for further processing. The extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the feature extraction model and then testing the model with different sets of input images. The simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed method achieves increased accuracy, precision, recall, and F1 score than the existing deep learning feature extraction methods. The performance improvement of the MBFE Algorithm technique in terms of accuracy, precision, recall, and F1 score is attained by 0.126%, 0.152%, 0.184%, and 0.38% with existing Back Propagation Neural Network (BPNN), Human Identification Using Wavelet Transform (HIUWT), Segmentation Methodology for Non-cooperative Recognition (SMNR), Daugman Iris Localization Algorithm (DILA) feature extraction techniques respectively.

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.

Multi-modal compound biometric feature set security and person authentication using cancelable 2D color barcode pattern generation technique

Multi-modal compound biometric feature set security and person authentication using cancelable 2D color barcode pattern generation technique

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.

An Efficient Multi-Modal Biometric Sensing and Authentication Framework for Distributed Applications

Multimodal biometrics is an emerging technology for distributed data security. Single and multi-user data authentication plays a vital role in commercial or e-governance applications. Many approaches have been implemented in literature to secure the single user data using biometric security systems. Most of these systems are based on static initialization parameters and fixed multi-modal biometric features for data authentication. Also, traditional multi-modal biometric based data authentication schemes are independent of dynamic variation in integrity verification. In order to overcome these problems, a new multi-user based multi-modal authentication framework is designed and implemented on large image data types. In this framework, different biometric features such as IRIS, facial and fingerprint features are used to find the unique integrity of user for data authentication and security process. A new integrity computational algorithm and encryption technique are implemented to provide the strong data integrity verification and data security in distributed applications. Experimental results show that the proposed multi-modal integrity-based encryption model has nearly 7% of computational integrity bit change and 5% of runtime on large dataset.

User Authentication System Based on Speech and Cascade Hybrid Facial Feature

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.

Extraction and relevance evaluation for multimodal biometric features

Extraction and relevance evaluation for multimodal biometric features

Integrated Framework for Anonymous Biometric Key Based Identity Management System

Implementation of measures to ensure security of transactions while ensuring privacy of user credentials is an area of challenge in digital transactions over a network. Integration of biometric pattern matching into an identity management system (IMS) enhances security of transactions and improves ease of use. Privacy of users in a biometric based system is improved by using keys generated directly from feature sets instead of conventional stored templates. This paper proposes a framework for integrating biometric key based authentication into an IMS.The generated keys need to be long, reproducible with high integrity and need to possess sufficient entropy. Generation of keys directly from feature traits poses a challenge due to intra and inter user variations inherent to biometric data. A novel methodology for generating and integrating crytpo keys into an identity management system is proposed. The keys have been extracted from iris trait. 300 bits keys have been extracted from iris datasets. The results are promising and can be extended to multi-modal biometric feature sets.

Proximal spatial vector and affinity coefficient for multimodal biometric secured social network communication

Using multimodalities, the field of biometric is receiving widespread acceptance for efficient communication between users in social network. Hence, suitable designing of authentication and access control is highly required to perform analysis of system in social network domain. In this work, a Proximal Spatial Vector and Affinity Coefficient (PSV-AC) framework is designed with the objective of ensuring authentic communication between users in social network using face and fingerprint. Initially, multimodal user biometric features with proximal feature pixel stored in spatial vector are gene encoded to obtain biometric identity keys. User authenticity is authorized by decoding gene encoded biometric features using biometric identity keys. User Affinity Coefficient is calculated via antecedent instances shared between different social users. The social network user’s affinity coefficient is ordered in a matrix. Matrix ranking is then performed to identify whether authentic user social communication happened in network. Experiments have been carried out using BioSecure datasets to measure the performance in terms of number of social users, social network authentication time, access control time, multimodal biometric feature template size, and true positive rate.

A Biometric-Secured Neighborhood Vector Relational Coefficient Framework for Social Network Communication

Multimodal Biometric has been widely researched to improve social network user communication. Appropriate modeling of authentication and access control is highly required to perform precise system analysis in a social network framework. In this work, a Multimodal Biometric Secured (MBSec) Social Network User Communication framework to provide user authentic social communication in a safe environment is investigated. The multimodal user biometric features (i.e. face and fingerprint) with neighborhood feature pixel dominancy stored in a spatial vector are gene encoded to generate biometric identity keys. The authenticity of user is validated by decoding gene encoded biometric features with biometric identity keys. User relational coefficient is calculated for users, using prior knowledge instances shared between different social users. The social network user’s relational coefficient is sequenced in a matrix to identify the occurrence of authentic user relational social communication. Experiments were conducted using face and fingerprint images collected from BioSecure dataset. Our results demonstrate that the proposed social network user communication framework is able to significantly improve the true positive rate of authenticated users, compared to conventional FR approaches that only make use of a single model. Further, we demonstrate that our MBSec framework has a low social network authentication time and access control time.

Security Management of Authentication System based on Multimodal Biometric Features

Security Management of Authentication System based on Multimodal Biometric Features

A Robust Multimodal Rank Level Fusion using Incremental Principal Component Analysis for Biometric Security

Biometric Authentication (BA) has been recently identified as a significant paradigm on maintaining the security level to improve individual person's authenticity. With more and more advanced technology, the BA has to be enhanced to cope up with the increased insecure environment. Several schemes employing multimodal biometric system fuses the face image with certain other traditional biometric modalities to ensure security. However changes in the facial expression significantly updates the facial geometry, thereby reduces the ranking score level. Some of the multimodal biometric research group has designed different algorithms on personal biometric feature based authentication. But, fusion multimodal biometric system did not provide robustness compromising the security level. To develop high robust multimodal biometric system, person multimodal biometric using Covariance Matrix Incremental Principal Component Analysis (CMI-PCA) method is proposed in this paper. The main goal of CMI-PCA method is to work with three principle steps and attain rank level fusion integration on the faces, ears and hand dorsal vein. First, the person's multimodal biometric features are sensed and feature extraction is performed using Gabor Filter based Incremental PCA to improve robustness level. Second step is to match the extracted features with stored test image from the database using Score Matching based on Covariance Matrix Incremental PCA. The score matching based on Covariance Matrix Incremental PCA maintain the scale of extracted features and compute the mean score value to match with test images without any Covariance Matrix range. Finally, the CMI-PCA method involves combining different biometric identification ranks for making final decision. The extended Borda Count Multimodal Ranking system is used in CMI-PCA to determine the integrated biometric outcome and ensures higher security level for individual information. Experiment is conducted on factors such as robustness level, multimodal matching score rate, rank level fusion efficiency rate.

Robust memory-efficient data level information fusion of multi-modal biometric images

This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image. The proposed approach reduces the memory size, increases the recognition accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms we developed, existing algorithms, and commercial algorithm. In addition to our multi-modal database, experiments are also performed on other well known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multi-modal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected significantly. The complexity of the fusion and the reconstruction algorithms is O( n log n) and is suitable for many real-time applications. We also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images.