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In heterogeneous wireless networks, the industrial Internet of Things (IIoT) is an essential contributor to increasing productivity and effectiveness. However, in various domains, such as industrial wireless scenarios, small cell domains, and vehicular ad hoc networks, an efficient and stable authentication algorithm is required (VANET). Specifically, IoT vehicles deal with vast amounts of data transmitted between VANET entities in different domains in such a large-scale environment. Also, crossing from one territory to another may have the connectivity services down for a while, leading to service interruption because it is pervasive in remote areas and places with multipath obstructions. Hence, it is vulnerable to specific attacks (e.g., replay attacks, modification attacks, man-in-the-middle attacks, and insider attacks), making the system inefficient. Also, high processing data increases the computation and communication cost, leading to an increased workload in the system. Thus, to solve the above issues, we propose an online/offline lightweight authentication scheme for the VANET cross-domain system in IIoT to improve the security and efficiency of the VANET. The proposed scheme utilizes an efficient AES-RSA algorithm to achieve integrity and confidentiality of the message. The offline joining is added to avoid remote network intrusions and the risk of network service interruptions. The proposed work includes two different significant goals to achieve first, then secure message on which the data is transmitted and efficiency in a cryptographic manner. The Burrows Abdi Needham (BAN logic) logic is used to prove that this scheme is mutually authenticated. The system's security has been tested using the well-known AVISPA tool to evaluate and verify its security formally. The results show that the proposed scheme outperforms the ID-CPPA, AAAS, and HCDA schemes by 53%, 55%, and 47% respectively in terms of computation cost, and 65%, 83%, and 40% respectively in ter

Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications, namely precision agriculture, environment, medical care, security, and surveillance. In the same vein, almost all of the known authentication techniques rely on the single gateway node, which is unsuitable for the current sensor nodes that are broadly distributed in the real world. Despite technological advances, resource constraints and vulnerability to an attacker physically capturing some sensor nodes have remained an important and challenging research field for developing wireless sensor network user authentication. This work proposes a new authentication scheme for agriculture professionals based on a multi-gateway communication model using a fuzzy extractor algorithm to support the Society 5.0 environment. The scheme provides a secure mutual authentication using the well-established formal method called BAN logic. The formal security verification of the proposed scheme is validated with the AVISPA tool, a powerful validation method for network security applications. In addition, the security of the scheme was informally analysed to demonstrate that the scheme is secure from different attacks, e.g., sensor capture, replay, and other network and physical attacks. Furthermore, the communication and computation costs of the proposed scheme are evaluated and show better performance than the existing authentication schemes.

Stereo vision systems that capture 3D space data are of great use as computer interface systems for emerging technologies such as VR and Robotics. They are inexpensive, yet practical implementation is challenged by high CPU processing power requirement. Thus, in this paper a scheme is developed to exploit GPUs in the processing of high definition images required to track an object position and orientation in 3D space in real-time. This is done by porting and optimizing essential processing algorithms to operate efficiently in parallel on a GPU. The object detected is a pen, with the position of the tip and it's orientation tracked in real-time. The writing captured by the system is than compared to the one from commercial digitizing tablet. The results achieved indicated that pen tip can be tracked in 3D space with a percentage error below 1.7% within a distance of 40cm from the stereo camera shooting at a frame rate of 30 frames per second.

Face recognition is essential for a wide range of technologies that requires person identification. Due to the presence of spoof face attacks, an additional layer of security is needed to protect the system, which can be provided by liveness detection. In this paper we develop a technique for discriminating live from fake images. Our approach is based upon the hypothesis that spoofing scheme leave statistical indication or structure in images which can be utilized for detection by assistance of image quality features. To achieve this, image quality measures (IQMs) statistical evaluation has been implemented using the analysis of variance (ANOVA) technique. A feature set of measures with highest discrimination power to distinguish between real and fake images was obtained. This ensures the simplicity of detection system and improves its computational efficiency.

A system that captures handwritten words on a piece of paper utilizing two cameras which observe and track a pen’s tip lateral movement is presented. The system tracks the pen’s tip in real-time based on the two images vision using colored markers. The stereo camera is calibrated for accurate threedimensional (3D) positioning of the pen’s tip. Pen’s tip 3D coordinates are then being used to re-construct the handwritten input into computer image. Experimental results show that the system can detect and track handwriting within 1mm accuracy on the y-axis and 7 mm accuracy on the x-axis (depth).

This paper proposes an effective approach to automatic recognition of printed Arabic numerals which are extracted from digital images. First, the input image is normalized and pre-processed to an acceptable form. From the preprocessed image, components of the words are segmented into individual objects representing different numbers. Second, the numerical recognition is performed using an expert system based on a set of if-else rules, where each set of rules represents the categorization of each number. Finally, rigorous experiments are carried out on 226 random Arabic numerals selected from 40 images of Iraqi car plate numbers. The proposed method attained an accuracy of 97%.

With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%.

Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images.

Gender as a soft biometric attribute has been extensively investigated in the domain of computer vision because of its numerous potential application areas. However, studies have shown that gender recognition performance can be hindered by improper alignment of facial images. As a result, previous experiments have adopted face alignment as an important stage in the recognition process, before performing feature extraction. In this paper, the problem of recognizing human gender from unaligned real world faces using single image per individual is investigated. The use of feature descriptor to form shape representation of face images with any arbitrary orientation from the cropped version of Labeled Faces in the Wild (LFW) dataset is proposed. By combining the feature extraction technique with artificial neural network for classification, a recognition rate of 89.3% is attained.

Hazardous road conditions such as potholes are usually reported by public either by phone or filling a form in the appropriate authorities website for the necessary actions to be taken. This required a longer time to solve the problem as the information provided may not be sufficient for action to be taken such as location and the picturing of the real situation might not be available. By implementing crowd sourcing concept in the smart phone application, it can provide a great combination as a tool to collect data from public. This paper proposed a mobile application that allows the public to easily report damages and potholes on roads to the appropriate authorities. This developed application has shortened the duration of relevant information received since user can immediately send to the authority's server through their mobile phone.

In the offline world, payments are often made over the counter with some levels of human inspections. However,such inspection does not exist in the online world. Online transactions are carried out virtually on a remote application server. Though other technical security measures have been introduced for online transactions such as the use of encryption, digital signatures and digital certificates, the issue of trust is still largely a major problem in e-commerce since actual authentication of users is not often established. A solution to this is to use biometrics Automatic Signature Verification (ASV) systems where human identification is carried out automatically based on their signatures.The main advantage of ASV over other biometrics technologies is that its applications are widely accepted and generally acknowledged by the public due to the fact that signatures have long been used as proof of identity in legal documents and financial transactions.Additionally, the ASV system allows the extraction of dynamic information that describes the way a signature is actually executed in terms of velocity, acceleration, pen pressure, pen inclination, etc. Many signature experts believe the dynamic information of the signing operation is generally consistent and stable throughout one’s lifetime.This in turn is more secure simply because it is harder to imitate human signing operation than to reproduce signature images of another person.Since ASV allows for remote networked authentication, it appears promising to most e-commerce applications. This paper generally describes the ASV potentials, its current applications and impediments in e-commerce related activities.It also addresses areas for ASV improvements.