Multimodal Biometric System Research Articles

An integrated approach for multimodal biometric recognition system using Pearson type-II (beta) distribution

Biometric recognition plays an important role in personnel identity authentication. Usually, biometric recognition protocols which involve single source of information are called unimodal systems. Such systems suffer from the problems like noisy sensor data, performance, collectability and non-universality. To have an accurate recognition, it is needed to develop a system with multimodal biometrics. Hence, in this paper, a new approach is proposed with the combination of multiple biometric traits such as face, fingerprint and palm vein. Region of interest (ROI) is used to consider the valuable information from the images. The 2D discrete cosine transform is used for extracting the feature vector from face, fingerprint and palm vein and fusion at feature extraction level. Here, the feature vector is modelled with Pearson type-II distribution and the model parameters are estimated using the EM algorithm. The initialisation of model parameters is done through moment method of estimators and K-means algorithm. The performance of the proposed algorithm is carried by experimentation with CASIA biometric database. Through experimentation, the proposed model performs more effectively than the algorithm with Gaussian mixture model.

A novel GAAC optimisation algorithm for multimodal fusion score decision making in secured biometric systems

Increased use of biometric systems on a global scale almost for all services have seen an increasing trend in research trying to improve the quality of authentication and containment of features extracted. A multimodal biometric system based on fusion score decision making has been proposed in this paper using a hybrid evolutionary framework. Genetic and ant colony optimisation (GAAC) algorithm has been presented and implemented on features of three biometric traits namely iris, fingerprint and finger vein to obtain a decision on the authenticity of the claiming individual. Features have been extracted using a frequency domain ridgelet transform as they are better able to approximate the fine component of ridges present on the fingerprint. The proposed hybrid technique is experimented on images from CASIA image database and efficiency metrics such as classification accuracy, positive find and negative find have been computed. The computational time has also been observed to be quite satisfactory due to fast converging nature of the hybrid combination.

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

Face–Iris Multimodal Biometric Identification System

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

Multimodal Image Fusion in Biometric Authentication

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

Fuzzy Logic Decision Fusion in a Fingerprints Based Multimodal Biometric System

Fuzzy Logic Decision Fusion in a Fingerprints Based Multimodal Biometric System

Quality based adaptive score fusion approach for multimodal biometric system

Multimodal Biometric Systems are extensively employed over unimodal counterparts for user authentication in the digital world. However, the application of multimodal systems to security-critical applications is limited mainly due to non-adaptiveness of these systems to the dynamic environment and inability to distinguish between spoofing attack and the noisy input image. In order to address these issues, a multimodal biometric system, which adaptively combines the scores from individual classifiers is proposed. For this, three modalities viz. face, finger, and iris are used to extract individual classifier scores. These classifier scores are adaptively fused considering that concurrent modalities are boosted and discordant modalities are suppressed. The conflicting belief among classifiers is resolved not only to achieve optimum fusion of classifier scores but also to cater dynamic environment. The proposed quality based score fusion also distinguish between spoofing attacks and noisy inputs as well. The performance of the proposed multimodal biometric system is experimentally validated using three chimeric multimodal databases. On an average, the proposed system achieves an accuracy of 99.5%, an EER of 0.5% and also outperforms state-of-the-art methods.

Adaptive Weighted Graph Approach to Generate Multimodal Cancelable Biometric Templates

Multimodal biometric systems offer numerous advantages over unimodal counterparts and are being used extensively in diverse applications. However, fusion of biometric data is a non-trivial task and curtail employability of multimodal systems for a varying set of biometric characteristics with different type and dimension. Moreover, comprehensive solutions against adversary attacks that ensure template protection and prevent presentation attacks are not in place. In this article, a secure multimodal cancelable biometric system is proposed to address these concerns. This approach introduces key images based generic feature extraction technique which reduces feature dimension and achieves revocability. The non-invertibility and unlinkability are ensured through cross-diffusion of complementary information from different modalities. A new feature fusion method based on an adaptive graph is proposed to generate multimodal cancelable biometric templates. Robustness against presentation attack is accomplished through quality based adaptation of features. Extensive experimentation is performed on benchmark databases for fingerprint, face, and iris, to illustrate the efficacy of multimodal cancelable templates. The proposed approach is shown to perform favorably against state-of-the-art feature fusion methods. Furthermore, the resilience of the proposed approach against security and privacy attacks is demonstrated.

User Specific Semi-Automatic Multimodal Biometric System based on Dynamic Fusion

User Specific Semi-Automatic Multimodal Biometric System based on Dynamic Fusion

Multibiometrics System Design Based on Feature Level Fusion

An efficient multimodal biometric system which combines biometric data originated from face, iris and signature biometrics has been presented. Proposed feature extraction algorithm for unimodal and multimodal system has been based on discrete wavelet transform. Among the various biometrics face and iris based human authentication system are proved reliable and efficient. Signature as a behavioral biometrics is very important in financial transaction. Signature has highest variability among all biometrics. This research work proposes an approach to combine signature biometrics with face and iris biometric. Proposed method fuses biometric information originated from face, iris and signature at feature level. Hamming distance based classifier has been used for classifying feature vector as a genuine or imposter. Proposed multibiometrics system has been evaluated on chimeric databases. It has been shown by the reported results that proposed multimodal system outperforms unimodal system performance. Proposed system has been analyzed for recognition rates and error rates. Performance of proposed multimodal system shows improvement in recognition rate and reduction in error.

A Robust Multimodal Biometric System VIA Multiple Svms

Generally single Support Vector Machine (SVM) is employed in existing multimodal biometric authentication techniques, and it assumes that whole set of the classifiers is available. But sometimes it is not possible due to some circumstances e.g. injury, some medical treatment etc. This paper includes a robust multimodal biometric authentication system that integrates FKP (Finger-Knuckle Print), face and fingerprint at matching score level fusion using multiple parallel Support Vector Machines (SVMs). Multiple SVMs are applied to overcome the problem of missing biometric modality. Every possible combination of three modalities (FKP, face and fingerprint) are taken into consideration and all combinations have a corresponding SVM to fuse the matching scores and produce the final score set for decision making. Proposed system is more flexible and robust as compared to existing multimodal biometric system with single SVM. The average accuracy of proposed system is estimated on a publicly available dataset with the use of MUBI tool(Multimodal Biometrics Integration tool) and MATLAB 2017b.

Implementing and analysing FAR and FRR for face and voice recognition (multimodal) using KNN classifier

PurposeThe purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone.Design/methodology/approachIn this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy.FindingsThe algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.Originality/valueThe algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

A Robust Multimodel Biometric Crypto System

Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it makes system more robust and secure. In this paper, a robust multimodal biometric crypto system has been proposed, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems. Experimental results are shown with the help of MATLAB3. 2017b.

Multimodal biometric system combining left and right palmprints

PurposePalmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems.Design/methodology/approachFeatures are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used.FindingsThe resulting feature vector length representing a person is compact and the runtime is reduced.Originality/valueIntensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

Verification of Biometric Traits using Deep Learning

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems including non-universality, noise, population coverage, vulnerability and intra-class variability for verification, authentication and identification of an individual. In this paper, the impact of deep learning in the field of biometrics is investigated where supervised learning is primarily involved in identifying biometric traits using Graphical User Interface. The trained deep learning system proposed is called MultiTraitConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without prior domain knowledge and classify the class of the biometric trait. A discriminative CNN training scheme is based on a combination of back-propagation algorithm and mini-batch Adam optimization method that is used for weights updating and learning rate adaption, respectively. To evaluate various CNN architecture data augmentation and dropout method training techniques have been used. A centralized database of different biometric trait images has been created using data sets of CASIA1 for iris trait, google 11K hand for palmprint trait, for face UTKFace is used. The accuracy of the proposed system is found to be 100%. Python library Keras is used to develop CNN model and TKinter is used to create GUI of the proposed system.

Multi-modal Iris Recognition System based on Convolution Neural Network

Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.

A Multimodal Biometrics for Aadhaar Based Secured E-Voting System

False polling is still a significant issue in elections in the latest moments. In this job, an effort is made to fix this issue using current Aadhaar card database and electoral biometrics. This scheme authenticates electors by combining multimodal biometrics such as picture, eye, and palm printing, after which registration is verified by verifying the age that enables only qualified applicants to register. The time needed for authentication is decreased by using the corresponding Aadhaar amount and multimodal biometrics. This is authentication will be achieved by inspecting whether the registered Aadhaar amount and multimodal biometrics match or not without linking it with the entire biometric database to boost authentication pace. The polling of ballots will be performed automatically so that space is decreased and results can be announced in less moment. The improvisations strive to increase the system's safety, efficiency, efficiency, scalability. This suggested a safe internet e-voting system (EVS) that utilizes as its backend UIDAI or Aadhaar database. In this job, the entry pictures are originally preprocessed and the removal of features is achieved using Improved Gabor filters. Enhanced Support Vector Machine Algorithm (eSVM) is used to match and classify features development of this scheme will render the polling method more comfortable and can, therefore, contribute to enhanced turn-out. Using this multimodal biometric system for voting purposes, election rigging was easily avoided.

Transformed Secure Feed Forward Supervised Learning Method for Authentication in Multi-Model Biometric System

Biometric is an automated detection of the characteristics of an individual on the basis of the biological and social features. Detection of the uni-modal biometric system is based on the biometric data of an individual. Some issue of distortion level spoofing threats are more accessible to biometric data. Some of the issues overcome by multimodal biometric scheme in which signature of the biometric data are determine for better security of the data. Multimodal biometric is used on variety of the application areas which are human computer interface, detection of the sensor through unique method. The physical and social characteristics are used for the identification of an individual using multimodal biometric system. Multi-model biometric system applications are security system developed in banking sectors, business phase and Industry (MNC) companies. In existing work, using ESVM method to recognize the biometric traits and problem occurs in existing phase is distortion and degrades the image quality present and reduces the recognition rate and high error rates. In proposed research, determined the biometric features finger print, face and iris through CASIA dataset. Then, distortion rate is recognised through salt and pepper method and removal of interference using filtration technique. After that, discrete wavelet transformation is used for the extraction of the features of the biometric system through face, fingerprint and eye that determine the graphical features. Along with that, feed forward neural network algorithm developed for classification and recognition of multi modal biometric behaviour characteristics. The Encrypted NN method conducts simulation work on the metrics like as a recognition rate, true positive rate and computation time. The experimental results demonstrate that Encrypted NN method is able to enhance the image quality, recognition rate and TPR and reduces the computational time of Multi-model Biometric System when compared with existing work and simulation tool used MATLAB 2016a.

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

A fusion scheme in multimodal biometric system is proposed in the name of Multimodal Feature Template Matching (MTM) system derived from the two picture fused values of average point and weighted assessment point. The main objective of MTM algorithm is to design the average point fusion and weighted average point by fusion of Iris and fingerprint features. The MTM algorithm is assigned with avgweights based on their verification accuracy. A hybrid approach of combining biometric image level information id presented. The raw information is merged at image level. This integration addressed, provides a small template and resilience to attacks. However it does not improve the recognition performance, but the performances is also less combination to its unimodal counterpart. Confidence level integration of compatible features of two different uncorrelated biometric traits fingerprint and iris provides sustainable improvement in performance accuracy compared to other integration methods as well as best unimodal system. Fingerprint and iris integration approaches presented in this proposed are more robust and reliable.

RETRACTED ARTICLE: Multi-modal biometric authentication system based on face and signature using legion feature estimation technique

In biometrics, picking of the right methodology is a testing errand for recognition of a person. Due to the advantage of widely accepted identification, face, and signature-based biometric modality are selected as a significant pattern as compared with other modalities. Different Face and signature successions of a similar subject may contain varieties in determination, light, pose, facial appearances and sign position. These varieties add to the difficulties in planning a viable multimodal-based face and signature recognition algorithm. This paper proposed about the face and signature recognition method from a large dataset with the different pose and multiple features. Face recognition is the first stage of a system then the signature verification will be done. Here, data glove signaling means of signing process are taken into account to do signature verification system. Hence the proposed work have used Face, and the corresponding signature is detected from data glove signal patterns to features-level fusion for the verification system. The proposed Legion feature based verification method will be developed using four important steps like, i) Preprocessing, ii) feature extraction from face and data glove signals, iii) Legion feature based feature matching through Euclidean distance, iv) Legion feature Neural network (LFNN) fusion based on weighted summation formulae where two weights will be optimally found out using Legion optimization algorithm, vi) Recognition based on the final score. Finally, based on the feature library the face image and signature can be recognized. The comparability estimation is finished by utilizing least Euclidean separation fusion based LFNN to decide perceived and non-perceived images. Also, in a similar examination, a proposed strategy is compared with current technique by several performance metrics and the proposed LFNN technique efficiently recognize the face images and corresponding signature from the input databases than the existing technology.