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Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results.

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

The importance of high-fidelity enhancement in low quality fingerprint image cannot be overemphasized. Most of the existing fingerprint enhancement methods are contextual filter-based methods and they often suffer from two shortcomings: (1) there is block effect on the enhanced images; and (2) they blur or destroy ridge structures around singular points. In order to well preserve the ridge structures in singular regions and avoid block effect, we develop a new method for fingerprint enhancement combining nontensor product wavelet filter banks and anisotropic filter. We first decompose the fingerprint image using the nontensor product wavelet filter banks. Then we modify the approximation subimage using anisotropic filtering and adjust the high frequency coefficients of the three other subimages by applying the adaptive approach to reduce the noises according to the geometry feature of images. Finally, the inverse transform is applied to map the result and a final contrast enhancement is done subsequently. Experiments have been conducted on the fingerprint database FVC2004 in our study. The results demonstrate that the proposed approach is capable of overcoming block effect and enhancing low quality fingerprint while preserving the ridge structures around singular points.

In the concrete implementation of the fuzzy vault algorithm, the geometric hash method is a common technique for automatic calibration of biometric templates. For the fuzzy problem of parameter acquisition, the matching accuracy of fuzzy vault template is affected in the three parameters: the pixel size, hash table and hash table quantization parameters ($\alpha$ and $\beta$). The single factor experiment method obtains the optimal range of these three parameters, and the extraction range of the fuzzy point and the selection rule of the base point distance are improved for the fuzzy vault algorithm. Finally, based on the FVC fingerprint database, their matching precision is compared for the algorithm before and after optimization. The experimental results show that the false rejection rate (FRR) of the optimized algorithm is reduced by at least 9.84%, and the false acceptance rate (FAR) is reduced by at least 7.12%, indicating that the optimization scheme improves the matching accuracy of the algorithm. The algorithm has certain robustness and practicability.

This paper presents a systematic literature review on optimizing feature extraction for palm and wrist multimodal biometrics. Identifying informative features across different modalities can be computationally expensive and time-consuming in such complex systems. Optimization techniques can streamline this process, making it more efficient thereby improving accuracy and reliability. The paper frames four research questions on input traits, approaches for feature extraction, classification approaches, and performance metrics of image data. The search query is generated based on the research questions that help retrieve the information on the above parameters. The focus of this paper is to provide the comprehensive and exhaustive gestalt of the appropriate input traits for image data from the information retrieved as well as optimal feature extraction and selection. However, the paper also intends to highlight the various classification approaches taken as well as the performance indicators against those classifiers. Further, the paper aims to analyze the effectiveness of various filtering techniques in eliminating image noise and improving overall system performance using MATLAB 2018. The paper concludes that a combination of palm and wrist biometrics could be a good input-trait combination. This work is novel as it covers multi-faceted processing, addressing various aspects of optimizing feature extraction and selection for palm and wrist multimodal biometrics.

Fingerprints are one of the simplest and most reliable human biometric features for identification. Geometry of the fingerprint is fractal and we can classify a fingerprint database with fractal dimension, but one can't identify a fingerprint with fractal dimension uniquely. In this paper we present a new approach for identifying fingerprint uniquely; for this purpose a new fractal is initially made from a fingerprint by using Fractal theory and Chaos Game theory. While making the new fractal, five parameters that can be used in identification process can be achieved. Finally a fractal is made for each fingerprint, and then by analyzing the new fractal and parameters obtained by Chaos Game, fingerprint identification can be performed. We called this method Fingerprint Fractal Identification System (FFIS). The presented method besides having features of fractals such as stability against turning, magnifying, deleting a part of image, etc. also has a desirable speed.

Issues with verification speed improvement to fingerprint systems are investigated in this paper. First, the impact of verification speed on the overall system performance is highlighted. Then a rather general speed enhancement procedure for algorithms is proposed, consisting of two or more pattern matching stages for refining the value of the discrimination function. Special attention is paid to the algorithm supporting the verification procedure used to determine the two verification thresholds. It is shown, and supported by numerical examples, that significant verification speed improvement can be achieved without sacrificing the system accuracy.

Recognizing individuals by their gait is a new biometric methodology, which employs dynamic features derived from tracking gait. Instead of the image processing techniques used in most existing studies, our previous study initialized the work of investigating gait recognition in terms of biomechanics. The experimental results showed that the angles and forces of the lower limb joints were reliable features for recognition of individuals, which can provide us with a considerable amount of information in the field of computer science and thus help in developing a more efficient recognition method, which is also more computationally efficient than current image processing methods. Encouraged by the early results, in this study, we proposed a people recognition method based on plantar pressure patterns, which can be used in a concealed manner. We hoped to prove the feasibility of using foot pressure for individual recognition.

Two different plantar pressure parameter measurement schemes are discussed: (1) the characteristic parameters and (2) the pressure values of each sensor cell in each frame. The self-organizing map (SOM) neuron network algorithm was used in both schemes for data classification. In order to improve the recognition rate, a support vector machine (SVM) was used as the data classification algorithm for the all-sensor-values method. High recognition rates were achieved with the second method, i.e., using all the sensor cell values of the foot pressure pattern during walking, regardless of the algorithm used. It is suggested that the foot pressure distribution of gait is a suitable feature for gait recognition. Both SOM and SVM can be feasible classifiers for foot pressure-based features.

Samples from stochastic signals having sufficient complexity need reveal only a little unexpected shared structure, in order to reject the hypothesis that they are independent. The mere failure of a test of statistical independence can thereby serve as a basis for recognizing stochastic patterns, provided they possess enough degrees-of-freedom, because all unrelated ones would pass such a test. This paper discusses exploitation of this statistical principle, combined with wavelet image coding methods to extract phase descriptions of incoherent patterns. Demodulation and coarse quantization of the phase information creates decision environments characterized by well-separated clusters, and this lends itself to rapid and reliable pattern recognition.

Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

Signature authentication with static and dynamic features of signature has been studied for decades, in this paper a novel and new method based on estimating elasticity and viscoelasticity characteristics of the muscles and tendons of index finger of the right hand was presented and the angles between the finger knuckles were collected by data collection glove and the location of digital pen tip on sensitive pad is stored in computer too. With NMC model and writing required mathematical equations and inverse modeling, physiological characteristics of muscles and tendons of right hand were estimated by LMS criteria. This approach has been applied on 30 right-hand persons that of each individual 5 genuine signature and some ordinary forgers to counterfeit genuine signature only by seeing the shape of original signature. 93.4% forgery signatures could have been recognized from genuine and only 6.6% could not have been detected. For verification, we used 5-fold cross-validation, with mean of EER$=$ 3.57 and standard deviation of EER$=$ 0.736. Therefore, we identified the physiological viscoelasticity and elasticity of muscles and tendons of hand as a new biometric.

In the recent several years, Lee et al. and Lin-Lai proposed fingerprint-based remote user authentication schemes using smart cards. But their schemes are vulnerable and susceptible to the attack and have practical pitfalls. Their schemes perform only unilateral authentication (only client authentication). In order to overcome the flaw, Khan and Zhang present a strong remote user authentication scheme by using fingerprint-biometric and smart cards. The proposed scheme is an extended and generalized form of ElGamal's signature scheme whose security is based on discrete logarithm problem, which is not yet forged. In addition, computational costs and efficiency of the proposed scheme are better than other related schemes. But as the time lapses, fingerprint of some people will be changed, such as being burned or being wounded. Once such case happened, the user won't be authentication. So we propose the authentication scheme of remote users by using multimodal biometric (fingerprint and face features) and smart cards. Proposed scheme not only overcome drawbacks and problems of previous schemes, but also provide a stronger and more secure authentication of remote users over insecure network.

This paper presents a new biometric approach to online personal identification using palm vein recognition technology. The system consists of two parts: a novel device for online palm vein image acquisition and an efficient algorithm for fast palm vein recognition. A data acquisition device were designed to capture the palm vein images under near-infrared (NIR) illuminations in less than 1 s. The feature extraction algorithm of palm vein image was Independent Component Analysis. Palm vein image matrix was projected to a new subspace based on Independent Component Analysis directly, the Euclidean distance of the projection matrix was calculated, nearest distance for classification was sought. The experiment was done in a self-build palm vein database. Firstly, this paper tested the proposed method by different component numbers, then compared the identification performance with the other typical palm vein recognition methods such as Gabor, SIFT, PCA+LPP. The experimental results show that this method can be applied online for fast speed and it has advantage in recognition performance.