False Rejection Ratio Research Articles

Towards Increasing Performance Rate with Minimized Dataset in ECG-based Human Identification

This paper presents an ECG-based human identification that aims to increase the prediction accuracy with a minimized training dataset using a Block Dense CNN (BDCNN) architecture. This study specifically examines the effect of increasing the number of individuals, while decreasing the amount of training data on the performance of the network. Furthermore, the performance of the proposed network with multisession ECG recordings is also investigated. Comprehensive experimentation on nine ECG public databases that include normal and various abnormal ECG beats has been carried out. Performance of the network is evaluated using metrics such as prediction accuracy, false rejection ratio (FRR), false acceptance ratio (FAR), computational complexity, and training time, and the results are then compared to the results of a residual network. Experimental results show that, not only in a single session but also in multisession ECG recordings, the proposed network proficiently overbears the load of increased persons with decreased training dataset with about a 5.06% increase in identification rate as compared to its close counterpart. Therefore, the proposed BDCNN can be a potential framework for ECG-based human identification systems.

Human Signature Verification System Using CNN with Tensor flow

One of the most popular verification biometrics is the signature. On checks, forms, letters, applications, minutes, and other documents, handwritten signatures are required. A person's handwritten signature must be individually identified because each individual's signature is unique by nature. Signature verification is a popular technique for confirming anyone's identity while they are not present. Human verification can be inaccurate and occasionally unsure. The use of Convolutional Neural Networks (CNN) for Writer-Dependent models in signature verification is examined in this research. In order to create forged signatures, random distortions were created in real photos using an auto encoder and then fed to the classifier during training. In addition to demonstrating various test outcomes for varying the number of training sets of images, the study describes all image pre-processing procedures that were applied to the image. In the Persian dataset, the system's average test accuracy is 83% after 22 real photos were used to train it. When the model was trained on nine real photos, accuracy dropped by 9.4%. Key Word: Offline Signature Verification, WD (Writer Dependent), CNN (Convolutional Neural Network), FAR (False Acceptance Ratio), FRR (False Rejection Ratio), Auto encoder

Hand geometry recognition: an approach for closed and separated fingers

<span lang="EN-US">Hand geometry has been a biometric trait that has attracted attention from several researchers. This stems from the fact that it is less intrusive and could be captured without contact with the acquisition device. Its application ranges from forensic examination to basic authentication use. However, restrictions in hand placement have proven to be one of its challenges. Users are either instructed to keep their fingers separate or closed during capture. Hence, this paper presents an approach to hand geometry using finger measurements that considers both closed and separate fingers. The system starts by cropping out the finger section of the hand and then resizing the cropped fingers. 20 distances were extracted from each finger in both separate and closed finger images. A comparison was made between Manhattan distance and Euclidean distance for features extraction. The support vector machine (SVM) was used for classification. The result showed a better result for Euclidean distance with a false acceptance ratio (FAR) of 0.6 and a false rejection ratio (FRR) of 1.2.</span>

Identification of Proper and Improper Signatures Using Graph Theory Techniques

Objectives: To propose an automatic signature identification for off-line signature utilising graph theory approaches. Methods: Scanned signatures (Kaggle, https://www.kaggle.com/divyanshrai/handwritten-signatures/data) are collected for off-line signature data. The method follows pre-processing, vertex point extraction by midpoint traverse method, features extraction using edge, average edge and average edge D-distance and Support Vector Machine (SVM) to classify and predict the true label for the genuine and forged signatures. False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) give the accuracy of the proposed methods. This off-line signature verification method is compared with the deep learning techniques existing in the literature. Findings: Support Vector Machine (SVM) used for classification and results on standard signature datasets like ICDAR (International Conference on Document Analysis and Recognition). The results demonstrate how the proposed strategy outperforms the state-of-the-art already available. Novelty: The proposed approach use the edge distance, average edge distance, and average edge D-distance inbuilt graph structures to extract the feature points. Keywords: Signature images; grid approach; bipartite graph; complete bipartite graph; mid point traverse method

IFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities

“Smart Cities” are a viable option to various issues caused by accelerated urban growth. To make smart cities a reality, smart citizens need to be connected to the “Smart City” through a digital ID. A digital ID allows citizens to make easy and effective use of amenities provided by the smart city such as healthcare, transport, finance, and energy. In this paper, we propose a proof-of-concept of facial authentication based end-to-end digital ID system for a smart city. Facial authentication systems are prone to various biometric template attacks and cyber security attacks. Our proposed system is designed to detect the first type of attack especially deepfake and presentation attacks. Users are authenticated each time before using facilities in the smart city. Authentication is performed on edge devices, making the process more secure as no data leaves the device during authentication. Facial data is stored at the cloud in a look up table format with an unidentifiable username. Our proposed solution achieved 97% accuracy in authentication with a False Rejection Ratio of 2% and False Acceptance Ratio of 3%.

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Objectives: The main objective is to propose a multimodal biometric system by forming a fusion of Face and Speech modalities using DTCWT+QFT techniques for face and MFCC+RASTA Techniques for Speech recognitions. The experimental results are compared with existing works and analysed the performance with counterparts. Methods: The proposed model, make use of DTCWT and QFT techniques to extract the features of face images and perform fusion of both. The MFCC and RASTA techniques are implemented to extract features of speech data and then fusion is applied. Various databases discussed and utilized for both face and speech recognition system proposed. Findings: The results of experimentation are compared with existing systems and analysis proved than the proposed system is placed in better position. The fusion of DTCWT and QFT techniques for face recognition system is implemented and the results using performance parameters such as False Acceptation Ratio (FAR), False Rejection Ratio (FRR), Total Success Rate (TSR), Partial Error Rate (PER), Equal Error Rate (EER) are tabulated for six different types of face data sets. The average performance of the results is compared with four existing fusion techniques and showed that the proposed system performs better. The fusion of MFCC and RASTA techniques for speech recognition system is implemented and the performance is measured by calculating accuracy, precision, recall and F1-score. These results are compared with five different schemes and proved that proposed system of fusion of face and speech traits works better for human recognitions. Novelty: Fusion of two algorithms for face recognition is implemented and the results analysed. Then the fusion of two algorithms for speech recognition is implemented and the results are analysed. The novel approach is presented to combine both face and speech recognition system in to single system to improve the security using multimodal biometrics. Keywords: DTCWT; QFT; RASTA; MFCC; Feature Extraction; Fusion

Improved two factor fuzzy commitment scheme for securing IoT device

PurposeGenerally, Internet-of-Things (IoT) is quite small sized with limited resource and low cost that may be vulnerable for physical and cloned attacking. All kind of authentication protocols designed to IoT devices are robust despite which it is prone to attack by hackers. In order to resolve this issue, there are various researches that have introduced the best method for obscuring the cryptographic key. However, the studies have majorly aimed to generate the key dynamically from noise data by Fuzzy Extractor (FE) or Fuzzy Commitment (FC). Hence, these methods have utilized this kind of data with noisy source namely Physical Unclonable Function (PUF) or biometric data. There are several IoT devices that get operated over undermined environment in which biometric data is not available but the technique utilized with biometric data can't be used to undermined IoT devices. Even though, the PUF technique is implemented for the undermined IoT devices this is quite vulnerable over physical attacks inclusive of accidental move and theft.Design/methodology/approachThis paper has proposed an advanced scheme in fuzzy commitment over IoT devices which is said to be Improved Two Factor Fuzzy Commitment Scheme (ITFFCS) and this proposed ITFFCS has used two kind of noisy factors present inside and outside the IoT devices. Though, an intruder has accomplished the IoT devices with an access to the internal noisy source, the intruder can't select an exact key from the available data which have been compared using comparable module as an interest.FindingsMoreover, the proposed ITFFC method results are compared with existing Static Random Accessible Memory (SRAM) PUF in enterprises application which illustrated the proposed ITFFC method with PUF has accomplished better results in parameters such as energy consumption, area utilization, False Acceptance Ratio (FAR) and Failure Rejection Ratio (FRR).Originality/valueThus, the proposed ITFFCS-PUF is comparatively better than existing method in both FAR and FRR with an average of 0.18% and 0.28%.

P‐77: LTPS TDDI with Full‐screen Fingerprint Recognition

A method of FTDI (Fingerprint Touch Display Integration), namely LTPS TDDI with full‐screen optical multi‐points fingerprint recognition function, is proposed. Display driven circuit, touch function and fingerprint image sensor are designed in the same LTPS TFT glass substrate. Partitions image collection design combined with skip‐frame driving are adopted to realize multi‐points fingerprint image collection. Photoconversion characteristics together with noise characteristics of fingerprint system are also presented. By improving sensitivity of APS circuit and decreasing readout noise, the FTDI can get a false rejection ratio <1.0% when single point fingerprint recognition mode is used.

Authentication of a User Using a Combination of Hand Gesture and Online Signature

In real world most of the biometric system shows authentication of user using uni-modal system. In this they use single source of information to authenticate the user such as face segmentation approach or mouse gesture dynamic approach or hand gesture or online signature approach. Some of the limitations exposed by uni-model system can be overcome by using combination of any two approaches. We have already implemented authentication of a user using hand gesture and online signature separately. In order to increase the level of security it is very much essential to combine two authentication modes. The main goal of this paper is to develop a model to authenticate a user using a combination of hand gesture and online signature. When user logs in, they need to register by using both authentication methods. First user is asked to record a gesture using accelerometer followed by signature using digital tablet and pen. Hand gesture device consisting of a tri-axial accelerometer and microcontroller transmission to sense the hand gesture and collect the same. Online signature device consisting of a digital tablet, pen and microcontroller transmission board to sense the online gesture and collect the same. Personal computer receives both gesture and signature as a input and stores in database. During verification user is asked to give both gesture and signature for verification. This work ensures better accuracy and more effectiveness in result and also helps to minimize system error rate. As a result the performance of combination method is 100% and false acceptance ratio and false rejection ratio is zero. Our work has been found to provide better accuracy when compared to existing works.

Multimodal access control system combining RFID, fingerprint and facial recognition

<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, multi-biometric aims to reduce the False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) which are two standard metrics widely used in the accuracy of biometric sys-tems. In this paper, we will examine the different possible scenario in multi-modal biometric systems using RFID, fingerprint and facial recognition, that can be adopted to merge information and improve the overall accuracy of the system.</span>

New Fusion Techniques to Extract Image Features and Recognition of Palm Vascular Pattern

A strong and efficient Feature extraction algorithm is highly recommended for individual recognition in human authentication systems. This paper presents the work carried on palm vein image to extract features of the person vein for recognition and classification using improved canny edge detector. This paper describes a novel method to extract valuable features of the people’s vein pattern and achieving high recognition rate. The experiments carried using two algorithms 1) PCACE (principal component analysis with canny edge) algorithm and 2) LDACE (linear discriminant analysis with canny edge) algorithm. These two methods are analyzed on palm vein image and found LDACE algorithm is a best extractor compare to PCACE method. An Equal Error Rate (EER) is applied to evaluate two algorithms. Hidden Markova Model (HMM) is utilized for image feature classification and matching using contactless Palm Under Test (PUT) palm vein database. The percentage of recognition is measured by False Acceptance Ratio (FAR) and False Rejection Ratio (FRR). This method shows robust response with respect to human palm vein identification process computation time and improved recognition rate.

Fuzzy Clustering and Classification based Iris Recognition: A Medical Application

The Iris based authentication framework is basically a pattern recognition strategy that utilizes iris designs, which are factually unique. In this study, an efficient iris recognition system is developed with the help of Possibilistic Fuzzy C-Means Clustering (PFCM) and Fuzzy Logic classifier (FLC). The proposed methodology consists of four modules namely, pre-processing, segmentation, normalization and recognition. Initially, in the pre-processing module, the input images are adjusted to do further processing. Then, we segment the iris region from the input image with the help of PFCM. After that, the segmented image is normalized with the help of the Daugman Robber Sheet Model (DRS). Finally, the iris image is recognized with the help of FLC. The performance of the proposed methodology is analyzed in terms of different metrics namely, accuracy, false acceptance ratio (FAR) and false rejection ratio (FRR). Experimental results demonstrate, proposed PFCM+FLC method attains a better accuracy of 97.5%

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), cross-matching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition system implemented in Matlab.

Combined global and local semantic feature–based image retrieval analysis with interactive feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

A highly secure Multi- Factor authentication system using biometrics to enhance privacy in Internet of Things (IOT)

Authentication is becoming critical in the Internet of Things (IoT) environment because of its many applications and services have been emerging in the areas such as smart city, healthcare, industry etc. Security and privacy plays a vital role in IoT because their services can be accessed through smart device applications by the user from everywhere and at any time. Hence a multi-factor based authentication can provide high security in IOT environment. This security system incorporates most of the valuable methods such as cryptography, steganography and pattern recognition for authentication process. Among various biometric traits, palm vein is more efficient because it has essential sufficient features points for individual unique identification. The system employs registration phase and authentication phase. The registration phase enrolls person privacy data with their biometric and the obtained data’s are encrypted with the help of Elliptical Curve Cryptography (ECC) and this confidential information is embedded into person palm print image using bits substitution procedure. In authentication phase, recognition will be performed through three levels such as password, palm print and One Time Password (OTP). Using these three levels the matching can be done. The texture features can be obtained by using Multi Block Local Binary Pattern (MB-LBP) and Gabor filter. To afford high authentication, OTP method is also appended. This system provides better information security and texture analysis rather than previous approaches. Thus this multiple level approach ensures a fool proof and a reliable way for data access. Results are in terms of some validation parameters like false acceptance ratio, false rejection ratio and recognition rate. Observing from results, it is clear that the proposed approach outperform many existing methods. As a result, the proposed scheme has strong security, reliability and enhanced computational efficiency.

An Improved Biometric Fusion System Based on Fingerprint and Face using Optimized Artificial Neural Network

This research presents an improved biometric fusion system (IBFS) that integrates fingerprint and face as a subsystem. Two authentication systems, namely, Improved Fingerprint Recognition System (IFPRS) and Improved Face Recognition System (IFRS), are introduced respectively. For both, Atmospheric Light Adjustment (ALA) algorithm is used as an image quality enhancement technique for the improvement in visualization of acquired fingerprint and face data. Genetic Algorithm (GA) is used as an optimization algorithm with minutiae feature for IFPRS and Speed Up Robust Feature (SURF) for IFRS. Artificial Neural Network (ANN) is used as a classifier for IBFS. For the demonstration of the results, quality based parameters are computed, and in the end, a comparison is drawn to depict the efficiency of the work.The optimization techniques such as Particle Swarm Optimization (PSO) and BFO (Bacterial Foraging Optimization) has been considered to determine the effectiveness of the proposed model.The experimental results consider different parameters such as False Acceptance Rate (FAR), False Rejection Ratio (FRR), Accuracy and Execution time which shows that performance of the proposed model better than the other optimization models. In addition, to enhance robustness of the proposed structure, the results further compared with conventional technique which shows that accuracy has been improved by 2%.

Development of High Recognition Rate FKP System using Fractional Cuckoo Search Optimization Method

Researchers have recently found that the finger knuckle print is an emerging and promising biometric identifier. In order to get better performance in a biometric system requires fusion of multiple instances within a modality. This paper makes use of fractional theory to propose an efficient feature level of fusion scheme named as a Fractional Cuckoo Search algorithm (FCS) for finger knuckle images. In this work both left index and right index features of an individual are extracted by using HOG method. These feature vectors are fused by optimal weight score selection, which finds out the weights from the proposed fractional cuckoo search algorithm. Thus the performance of the proposed system has been evaluated with the help of multi- layered SVM classifier in terms of False Acceptance Ratio (FAR), False Rejection Ratio (FRR) and Accuracy. From the experimental results, our proposed optimization system obtains a better accuracy of 98.97%.

Enhanced Authentication Using Hybrid PUF with FSM for Protecting IPs of SoC FPGAs

A new generation of technology is harder and costlier to deliver because of the physical design limitations of the silicon chip. The minute chip alone is not only compromising the requirements of the user but also creates challenges with respect to security. Architecture for two-factor authentication is designed with low-power, area and with less- human intervention. The proposed model consists of hybrid physical unclonable functions (PUFs) and finite state machine (FSM), which is used to secure the chip and intellectual property (IP) respectively. The PUFs are most often used in recent security applications such as IP protection, IC metering, hardware signature, and obfuscation. This application needs a complex algorithm with a database which consumes more cost and time. In this paper, we have proposed an authentication model consisting of strong and weak PUF with an FSM which can be used for IoT applications. The main focus of this proposal is to authenticate hardware and software IP in circuits. The Experimental evaluation illustrates that the area and power consumed are 5% and 9%, respectively, for authenticating 26 IPs with no false acceptance ratio (FAR) and 1% false rejection ratio (FRR).

Multi-Modal Biometric: Bi-Directional Empirical Mode Decomposition with Hilbert-Hung Transformation

Biometric systems (BS) helps in reorganization of individual person based on the biological traits like ears, veins, signatures, voices, typing styles, gaits, etc. As, the Uni-modal BS does not give better security and recognition accuracy, the multimodal BS is introduced. In this paper, biological characters like face, finger print and iris are used in the feature level fusion based multimodal BS to overcome those issues. The feature extraction is performed by Bi-directional Empirical Mode Decomposition (BEMD) and Grey Level Co-occurrence Matrix (GLCM) algorithm. Hilbert-Huang transform (HHT) is applied after feature extraction to obtain local features such as local amplitude and phase. The combination of BEMD, HHT and GLCM are used for achieving effective accuracy in the clas-sification process. MMB-BEMD-HHT method is used in Multi-class support vector machine technique (MC-SVM) as a classifier. The false rejection ratio has improved using feature level fusion (FLF) and MC-SVM technique. The performance of MMB-BEMD-HHT method is measured based on the parameters like False Acceptance Ratio (FAR), False Rejection Ratio (FRR), and accuracy and compared it with an existing system. The MMB-BEMD-HHT method gave 96% of accuracy for identifying the biometric traits of individual persons.

Novel approach for sleep disorder monitoring using a finite‐state machine for localities lacking specialist physicians

This study proposes a novel method for sleep disorder monitoring based on a finite-state machine (FSM) from various bio-signals, namely electroencephalography (EEG), electro-oculography (EOG) and electromyography (EMG) signals. The sleep signals have been obtained from physionet sleep repository, which includes horizontal EOG, submental-EMG and EEG sampled at 100 Hz sampling frequency. Inputs given to the FSM-based module are the processed signals from EMG, EEG and EOG signals. The FSM module for sleep analysis is composed of different states and the conditions to flow from one state to another state. In this study, two FSM modules are designed, one for sleep wave and another for sleep stage identification. Based on the outputs obtained from the above two FSM modules, the sleep disorder can be monitored. The accuracy of the proposed method has been calculated with percentage accuracy, false acceptance rate and false rejection ratio. The average classification accuracy of the finite-state automaton (FSA)-based method is up to 99% for all the tested fault cases. The proposed FSA method suggests a novel method and can be put to effective use in the rural areas for primary analysis.