Intelligent Face Research Articles

Enhancing Mission Planning of Large-Scale UAV Swarms with Ensemble Predictive Model

Target assignment and trajectory planning are two crucial components of mission planning for unmanned aerial vehicle (UAV) swarms. In large-scale missions, the significance of planning efficiency becomes more pronounced. However, existing planning algorithms based on evolutionary computation and swarm intelligence face formidable challenges in terms of both efficiency and effectiveness. Additionally, the extensive trajectory planning involved is a significant factor affecting efficiency. Therefore, this paper proposes a dedicated method for large-scale mission planning. Firstly, to avoid extensive trajectory planning operations, this paper suggests utilizing a machine learning algorithm to establish a predictive model of trajectory length. To ensure predictive accuracy, an ensemble algorithm based on Gaussian process regression (GPR) is proposed. Secondly, to ensure the efficiency and effectiveness of target assignments in large-scale missions, this paper draws inspiration from a greedy search and proposes a simple yet effective target assignment algorithm. This algorithm can effectively handle a large number of decision variables and constraints involved in large-scale missions. Finally, we validated the effectiveness of the proposed method through 15 simulated missions of different scales. Among the 10 medium- to large-scale missions, our method achieved the best results in 9 of them, demonstrating the competitive advantage of our method in large-scale missions. Comparative results demonstrate the advantage of the proposed methods from both prediction and mission planning perspectives.

The two faces of Artificial Intelligence (AI): Analyzing how AI usage shapes employee behaviors in the hospitality industry

AI has become a critical technology driving societal advancement and impacting various sectors, including hospitality. Technology has social attributes, and employees play a major role in its application. Exploring the impact of AI usage on employee behaviors is essential. Drawing on the conservation of resources theory and the approach/avoidance systems theory, this study constructs a dual-path model to explore the effect of AI usage on employee behaviors. Data from 280 hotel employees in China were collected at three time points. Results show that AI usage enhance technological self-efficacy of employees, thereby promoting prosocial service behavior. Conversely, it could also induce workplace anxiety of employees, consequently leading to work withdrawal behavior. An approach orientation amplified the impact of AI usage on technological self-efficacy, while an avoidance orientation heightened the impact of AI usage on workplace anxiety. The study discussed the theoretical and practical implications and offers suggestions for future research.

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Research On the Development of Intelligent Face Recognition System for Business Competition in Chatgpt Industry

Artificial intelligence has become a key area of today's international competition. Analysis of the development trend of artificial intelligence and the strategies of major powers is crucial to winning this competition. This paper refers to (GCN) and Laplace's (LBP) feature equation in ChatGPT. Firstly, an image processing method based on LBP is proposed. GCN analyzes the temporal and spatial correlation of character attributes in the time domain. This method can capture both short-term and long-term temporal and situational correlations simultaneously, effectively improving the extraction of temporal and situational correlations. Then, text analysis determines the implementation intentions, measures and prospects of the EU's "human-centered" artificial intelligence competition strategy. It also evaluates its strategic effects from the relevant reactions of its main competitor, the United States, and infers the weaknesses and risks existing in the strategy.

High-efficient and intelligent antibacterial face mask integrated with airflow-temperature dual-function sensors for respiratory monitoring and disease prevention

Intelligent face masks play crucial roles in health monitoring and disease prevention, having attracted huge attention in recent years. However, most of the current intelligent face masks focus on monitoring single physical signal, which were unable to provide comprehensive information. Herein, an intelligent face mask with airflow and temperature sensing abilities, high-efficiency filtration and excellent antibacterial activity was proposed. The real-time airflow monitoring was realized by a triboelectric nanogenerator (TENG), which was composed of electrospun nanofibrous membrane and polydimethylsiloxane (PDMS) composite film. The fabricated electrospun nanofibrous membrane simultaneously played roles as tribo-positive material, filter and antibacterial membrane. The PDMS composite film prepared by co-blending and surface modification was applied as tribo-negative material. It was found that the combination of co-blending and surface modification enhanced the tribo-negative property of the PDMS film, resulting in an increment of output performance of TENG. The TENG integrated into a face mask could monitor respiratory rate and respiration intensity in real time. Additionally, the temperature sensing was achieved by a serpentine PDMS/laser-induced graphene temperature sensor. The temperature sensor exhibited a temperature coefficient of resistance of 0.316% °C−1, which could detect subtle temperature variations. Furthermore, the electrospun nanofibrous membrane exhibited excellent filtration performance and antibacterial activity. Therefore, the prepared intelligent face mask showed promising potential for healthcare applications.

Intelligent Face Recognition Based Students’ Attendance System

This paper aims at developing an intelligent face recognition students’ attendance system to enhance school attendance tracking. The system is made up of four phases- database of students’ details, face detection, face recognition and attendance report. The database stores all the students’ details and images of the students’ face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required.

Research on the design of automatic image processing function for intelligent face management system

Abstract Around the background of rapid development of intelligent technology, an efficient image processing system oriented to the smart management of human faces is focused on. The system is mainly developed towards high speed, high definition, high integration and reliability. The article investigates a novel automatic image processing algorithm, covering three key modules: automatic exposure control, color interpolation and chromaticity space conversion. The algorithm can process the output image of CMOS sensor in Bayer format in real time and adjust the image parameters to obtain a high-quality image. In terms of face recognition performance, the algorithm has a significant advantage in recognition speed compared with other algorithms, and the average recognition accuracy reaches 94.258%. In the practical application of ID card portrait processing, the image shows a more uniform grayscale distribution in the range of 5 to 255 after automatic color adjustment, and the color quality is significantly improved. Meanwhile, in the portrait enhancement experiments, the images obtained with this image automatic processing algorithm outperform the traditional ID card image processing methods regarding information entropy, mutual information, standard deviation and peak signal-to-noise ratio (PSNR).

Retracted: English Web-Based Teaching Supervision Based on Intelligent Face Image Perception and Processing for IoT

Retracted: English Web-Based Teaching Supervision Based on Intelligent Face Image Perception and Processing for IoT

Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model

Face age progression aims to change an individual’s face from a provided face image to forecast how that image will look in the future. Face aging is gaining much attention in today’s environment, which needs better security and a touchless unique identification mechanism. Researchers are focused on creating face processing algorithms to address the difficulty of producing realistic aged faces for smart system applications over the earlier decades. In the literature, the two basic needs of face age progression, aging accuracy, and identity preservation are not thoroughly addressed. According to the extraordinary gains in image synthesis made by deep generative methods and their significant influence on a wide variety of practical applications such as identifying missing persons using entertainment, childhood images, and so on, face age progression/regression has reawakened attention. The majority of present techniques concentrate on face age progression and is beneficial and productive in learning the transition across age groups utilizing paired data, i.e., face images of the similar individual at various ages. Through the motivation of the important success attained by Generative Adversarial Networks (GANs), this paper uses tactics to implement the improved Cycle GAN-based intelligent face age progression model. Initially, the standard datasets for the face progression are gathered, and the face is detected using the Viola-Jones object detection algorithm. Then, the pre-processing of the facial image is performed by median filtering and contrast enhancement techniques. Once the image is pre-processed, the Hyperparameter Tuning-Cycle GAN (HT-Cycle GAN) is adopted for face age progression. As an improvement, the hyperparameters of the Cycle GAN are optimized or tuned by the modified Galactic Swarm Optimization (GSO), known as Best Fitness-based Galactic Swarm Optimization (BF-GSO). From the evaluation of statistical analysis, the similarity score of BF-GSO-HT-CycleGAN is 0.80%, 3.33%, and 2.86% higher than cGAN, CycleGAN, and Dubbed FaceGAN, respectively. Here, the Dubbed FaceGAN is the 2nd greatest network. Furthermore, compared to traditional models utilizing distinct standard datasets, the experimental findings show that the suggested technique attains efficiency, accuracy, and flexibility.

Intelligent face sketch recognition system using shearlet transform and convolutional neural network model

Intelligent face sketch recognition system using shearlet transform and convolutional neural network model

Application of improved CNN-based face detection technology in public administration

In public management, intelligent face recognition detection technology plays a very crucial role, which can greatly improve the efficiency of public management and reduce the workload of staff. To address the shortcomings of traditional face detection algorithms such as low detection efficiency and easy overfitting, a face detection model based on convolutional neural network (CNN) was proposed in this study, and the structure of CNN was optimized to enhance the accuracy and efficiency of the proposed face detection model. To solve the face detection errors caused by illumination differences, a light compensation strategy was proposed to pre-process the data; meanwhile, a Gaussian curvature filtering algorithm was used to enhance the face image and improve the subsequent detection accuracy. On this basis, a face detection model based on improved CNN was designed in this study. Experiments showed that the accuracy of the model reached 99.86% with high accuracy and efficiency, indicating that such method can improve the efficiency of public management and has good application prospects in access control and check-in systems.

Repurposing Artificial Intelligence Tools for Disease Modeling: Case Study of Face Recognition Deficits in Neurodegenerative Diseases.

Face recognition deficits occur in diseases such as prosopagnosia, autism, Alzheimer's disease, and dementias. The objective of this study was to evaluate whether degrading the architecture of artificial intelligence (AI) face recognition algorithms can model deficits in diseases. Two established face recognition models, convolutional-classification neural network (C-CNN) and Siamese network (SN), were trained on the FEI faces data set (~ 14 images/person for 200 persons). The trained networks were perturbed by reducing weights (weakening) and node count (lesioning) to emulate brain tissue dysfunction and lesions, respectively. Accuracy assessments were used as surrogates for face recognition deficits. The findings were compared with clinical outcomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Face recognition accuracy decreased gradually for weakening factors less than 0.55 for C-CNN, and 0.85 for SN. Rapid accuracy loss occurred at higher values. C-CNN accuracy was similarly affected by weakening any convolutional layer whereas SN accuracy was more sensitive to weakening of the first convolutional layer. SN accuracy declined gradually with a rapid drop when nearly all nodes were lesioned. C-CNN accuracy declined rapidly when as few as 10% of nodes were lesioned. CNN and SN were more sensitive to lesioning of the first convolutional layer. Overall, SN was more robust than C-CNN, and the findings from SN experiments were concordant with ADNI results. As predicted from modeling, brain network failure quotient was related to key clinical outcome measures for cognition and functioning. Perturbation of AI networks is a promising method for modeling disease progression effects on complex cognitive outcomes.

Ensuring Academic Integrity and Trust in Online Learning Environments: A Longitudinal Study of an AI-Centered Proctoring System in Tertiary Educational Institutions

The credibility of online examinations in Higher Education is hardened by numerous factors and use-case scenarios. This paper reports on a longitudinal study, that spanned over eighteen months, in which various stakeholders from three European Higher Education Institutions (HEIs) participated, aiming to identify core threat scenarios experienced during online examinations, and to, accordingly, propose threat models, data metrics and countermeasure features that HEI learning management systems can embrace to address the identified threat scenarios. We also report on a feasibility study of an open-source intelligent and continuous student identity management system, namely TRUSTID, which implements the identified data metrics and countermeasures. A user evaluation with HEI students (n = 133) revealed that the TRUSTID system is resilient and effective against impersonation attacks, based on intelligent face and voice identification mechanisms, and scored well in usability and user experience. Aspects concerning the preservation of privacy in storing, retrieving and processing sensitive personal data are also discussed.

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

Modelling the biology behind animal behaviour has attracted great interest in recent years. Nevertheless, neuroscience and artificial intelligence face the challenge of representing and emulating animal behaviour in robots. Consequently, this paper presents a biologically inspired motivational model to control the biological functions of autonomous robots that interact with and emulate human behaviour. The model is intended to produce fully autonomous, natural, and behaviour that can adapt to both familiar and unexpected situations in human–robot interactions. The primary contribution of this paper is to present novel methods for modelling the robot’s internal state to generate deliberative and reactive behaviour, how it perceives and evaluates the stimuli from the environment, and the role of emotional responses. Our architecture emulates essential animal biological functions such as neuroendocrine responses, circadian and ultradian rhythms, motivation, and affection, to generate biologically inspired behaviour in social robots. Neuroendocrinal substances control biological functions such as sleep, wakefulness, and emotion. Deficits in these processes regulate the robot’s motivational and affective states, significantly influencing the robot’s decision-making and, therefore, its behaviour. We evaluated the model by observing the long-term behaviour of the social robot Mini while interacting with people. The experiment assessed how the robot’s behaviour varied and evolved depending on its internal variables and external situations, adapting to different conditions. The outcomes show that an autonomous robot with appropriate decision-making can cope with its internal deficits and unexpected situations, controlling its sleep–wake cycle, social behaviour, affective states, and stress, when acting in human–robot interactions.

Enhancing Smart Home Security with Face Recognition using Deep Learning

Abstract: Recently, security has been a growing concern for human life, and the cost is a significant concern. The research recommends implementing a real-time recognition system to rapidly handle photographs to reduce monitoring expenses and ensure safety in homes and offices by identifying people. The main goal of this study was to create an intelligent face recognition approach using deep learning for intelligent homes. To illustrate the study's effectiveness, the suggested model is contrasted with other cutting-edge techniques. The research introduces a tree-based deep model for facial recognition in the Cloud, which requires fewer computing resources but maintains accuracy. The input volume of the model is partitioned into multiple volumes, and trees are created for each volume according to their height and number of branches. A residual function, constructed from a convolutional layer and two non-linear functions, represents each branch. The proposed model is assessed on various publicly accessible databases and compared with the industry's best deep models for facial recognition.

Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment. When finding a suspicious person, the tracked object cannot be locked in time for tracking. This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person. This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system. In this article, we proposed a TIMT (The Intelligent Monitoring and Tracking) algorithm which can make the drone have smart surveillance and tracking capabilities. It combined with Artificial Intelligent (AI) face recognition technology and the OpenPose which is able to monitor the physical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote control interface of the drone. This system is highly agile and could be adjusted immediately to any angle and screen that we monitor. Therefore, the system could find abnormal conditions immediately and track and monitor them automatically. That is the system can immediately detect when someone invades the home or community, and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved. Experimental results show that the intelligent monitoring and tracking drone system has an excellent performance, which not only dramatically reduces the number of monitors and the required equipment but also achieves perfect monitoring and tracking.

Retracted: Intelligent Face Recognition System Based on Universal Design Concept.

[This retracts the article DOI: 10.1155/2022/8633846.].

Intelligent Face Recognition Based Multi-Location Linked IoT Based Car Parking System

Intelligent Face Recognition Based Multi-Location Linked IoT Based Car Parking System

Retraction Note: Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things

Retraction Note: Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the “Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a “Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2.