Portals to the Soul: Facial Recognition Technologies and Chinese Portraiture

With the development of society, all parties are facing increasingly urgent requirements for fast and effective automatic identity verification. Since biological characteristics are intrinsic properties of humans, they have strong stability and individual differences, so they are an ideal basis for identity verification. Compared with biometrics such as fingerprints and iris, face recognition is more direct, friendly, and convenient to be accepted by users. Therefore, face recognition has become a current research hotspot in the field of pattern recognition and artificial intelligence. As an important part of human-computer interaction, the research of face detection and recognition technology has great theoretical significance and application value. In this paper, the face detection method based on skin color information and the face recognition algorithm based on the embedded hidden Markov model are deeply studied, and the development of the development of the face detection and recognition system, performance analysis and specific applications are done. Introduced. In this paper, the detection and positioning of the face and the extraction of facial features are carried out on the frontal color image: the feature vector extraction of the detected face region using the two-dimensional discrete cosine transform and the method based on the embedded hidden Markov model are proposed. Face recognition method. Experiments show that the method of using the embedded hidden Markov model based on two-dimensional discrete cosine transform for face recognition can make better use of the two-dimensional statistical characteristics of face images, and has high recognition efficiency and very high recognition efficiency. Good robustness; researched on the recognition of hand-drawn portraits of human faces, and proposed a novel algorithm for converting photo images into portraits; applied the face detection and recognition system to specific practices, and established An intelligent search system for the source of unknown corpses. This system provides strong technical support and guarantee for the public security department to detect cases, and has important research and development significance.

Through the combination of computer vision technology and artificial intelligence, facial recognition technology is drawing attention as a new means of personal authentication in the era of the fourth industry. Facial recognition technology uses imaging equipment to photograph a person"s face and extract characteristic data. The extracted data are matched against the facial features of the stored database. Facial recognition technology is a contactless technology compared to other biometric recognition technologies, which is used in various fields due to its high hygiene, convenience and security, and in particular, safety accidents in workplaces are closely related to life, and various studies related to workplace safety management using intelligent video information are being conducted in the manufacturing industry. In this paper, a study is conducted on the development of facial recognition algorithm using deep learning to control worker access in hazardous areas. The accuracy of the recognition of the proposed facial recognition algorithm (object detection algorithm (SSD) and object recognition algorithm (ResNet)) is closely related to the safety of the operator. Therefore, the goal is to analyze the relationship between various normalization techniques (Min-Max Scaler, MaxAbs Scaler, Standard Scaler) and the recognition rate of the proposed facial recognition algorithm to propose a high-accuracy facial recognition algorithm. In the future, we will conduct research on safety issues in the manufacturing industry based on facial recognition and image recognition technologies.

Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.

The facial expressions of students in the class are one of the most direct and effective ways to reflect the emotional investment of learning. Driven by a new generation of artificial intelligence technology, facial expression recognition in classroom and learning emotion analysis based on video image comprehension has become a hot topic in the field of Wisdom Education. In the wisdom classroom environment, the emotional state of the students in classroom can be effectively recognized and perceived through facial expression recognition technology, so as to capture the emotional input of the students in the classroom in real-time. This provides an effective means to achieve differentiating instruction, personalized learning, and accurate teaching decision-making. Based on a large number of frontier research and recent achievements on domestic and overseas, this paper makes a comprehensive review of the technology of facial recognition and the study of emotional analysis in classroom. Firstly, the classification of students' expressions and the construction of the database are summarized and analyzed. Secondly, this paper summarizes the research status, key points, and development trend of class facial recognition technology based on deep learning in ideal and non-ideal scenarios. Thirdly, based on the classical emotion analysis model, the typical application of emotion analysis based on students' facial recognition is discussed in detail. Finally, this paper summarizes the current challenges and future research directions of emotion recognition technology and learning emotion analysis in classroom. It provides references and guidance for the research in this field.

Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like marketing, health care, behavioral analysis and also in human-machine interaction. Facial expression recognition helps a lot more than facial recognition. It helps the retailers to understand their customers, doctors to understand their patients, and organizations to understand their clients. For the expression recognition, we are using the landmarks of face which are appearance-based features. With the use of an active shape model, LBP (Local Binary Patterns) derives its properties from face landmarks. The operation is carried out by taking into account pixel values, which improves the rate of expression recognition. In an experiment done using previous methods and 10-fold cross validation, the accuracy achieved is 89.71%. CK+ Database is used to achieve this result.

with the Explosive Development of Deep Learning Technology Face Recognition and Other Recognition Technologies Mostly Adopt Deep Learning Algorithm for Recognition. Although the Deep Learning Algorithm Has High Recognition Accuracy, It Has a Huge Demand for Computing. in the Mobile Terminal, We Can Use Artificial Intelligence Chips That Can Accelerate Deep Learning Operations to Complete Relevant Operations. Deep Learning Has Fixed Modes, Like Convolution. Ai Chips Can Significantly Improve the Efficiency of Deep Learning Operations by Optimizing the Corresponding Operation Modes. in This Way, Mobile Terminals Can Quickly Implement Complex Deep Learning Operations, Such as Face Recognition Based on Deep Learning. One Representative of Ai Chips is the Tensor Processing Unit of Google, Which is Able to Accelerate the Tensor Flow of the Deep Learning System, Which is Far More Efficient Than Gnus. the Tpu Provides 1,530 Times the Performance Improvement and 3,080 Times the Efficiency (Performance/Watt) Improvement over the Same Cpu and Cpu. Traditional Face Recognition Algorithms Include Face Recognition Technology Based on Pca(Principal Components Analysis) and Face Location Technology Based on Ad Boost. Although the Traditional Face Recognition Technology is Fast, the Detection Effect is Much Different from the Deep Learning Technology. on the One Hand, the Accuracy of the Traditional Face Recognition Methods Represented by Pca is Far Lower Than That of the Deep Learning Algorithm. on the Other Hand, for the Recognition of Massive Users, the Traditional Pca Face Recognition Technology is Not Competent.

With the development of science and technology, computer science is becoming more and more mature and plays an important role in social life and economic development. Based on this background, an epidemic prevention and control information platform for universities based on face recognition technology and temperature technology has been created. The system can realize real-time monitoring of students' identity verification, ID card numbers and internal school item files, it can also effectively detect the body temperature of teachers and students, and make timely early warning suggestions to relevant departments based on the detection results, which provides effective data support as well as technical guarantee in public security work. At the same time, it uses embedded computer technology to realize the interaction of data between each link in the epidemic prevention work and the database, which is convenient for the staff to grasp the relevant data information and dynamic changes in a timely manner, thus providing effective guarantee measures for the management of universities. This paper introduces the information platform of university epidemic prevention and control based on face recognition and temperature recognition technology, and designs and implements the study of the system.

Guest Editorial Special Issue on Mobile Biometrics

We see faces every day, and all of them leave us with different impressions. Our brains also respond emotionally to new and familiar faces we find in non-animated objects, paintings, and sculptures. To retain such memory of a face or express our feelings, we create portraits. Portraits have fascinated us for millennia. This paper reviews the interrelationships between painting, photography, facial recognition, and artificial intelligence technologies in portraiture aesthetics. The importance of portraits as a subject in artistic creation, studies, and research has led to various advancements in technological innovations. The inevitable role that portraits play in different mediums of art, history of art, communication, and security marks the intersection between humanity and identity, art and technology, as well as its undeniable position within the genre of art. Today the rapid development of digital tools, mobile software, and artificial intelligence allows not only the artist and designer to create portraitures but is also widely used by the public in all walks of life to create portraitures instantly. Driven by the marriage and momentum of art and technology in the field of new media art, will artificial intelligence and modern facial recognition technology take over the role of artists? Keywords: portrait, painting, photography, facial recognition technology, artificial intelligence

With the continuous development of computer, many advanced technologies are mostly based on computer vision, face recognition technology plays an important role. The camera captures the image or video stream containing the human face, and detects and tracks the face in the image. This series of related technologies are also called face detection and recognition. The method of face recognition is more natural, more intuitive, and has the characteristics of indirect and concurrency. It has been widely used in many fields. Now, computer vision is an active research field. In this paper, we study the methods of face detection and recognition in the field of computer vision. The user interface is designed with PyQt, and the function of face model training, reading the image to be recognized, extracting the image to be recognized and the recognition of human face are realized. The experimental results show that the proposed method has high recognition efficiency, and a complete algorithm system based on face tracking detection and face recognition is realized, which can lay a good foundation for future research on the aspect of vision.

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person’s face into a face print, which is then stored in a database to verify an individual’s identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications.

This article focuses on the application of artificial intelligence in tourism visual marketing, emphasizing the practical forms, opportunities, and challenges of facial recognition and facial emotion recognition technology. Facial recognition achieves precise identity and interest matching, while facial emotion recognition analyzes real-time expressions to determine emotional states. Together, they can deliver personalized services, enhance interactive experiences, optimize advertising placements, and product development, bringing opportunities such as improved customer experience, increased operational efficiency, enhanced market competitiveness, and innovative products and services to the tourism industry. [14] However, the application of these technologies also faces challenges such as data privacy leaks, insufficient recognition accuracy, and social ethical controversies. This study aims to provide practical guidelines for tourism companies to formulate AI visual marketing strategies, helping to balance technological application with visitor rights and achieve sustainable development.

Employee satisfaction with the compensation system is a critical factor influencing organizational productivity, employee retention, and overall workplace morale. Traditional payroll management systems, often plagued by inaccuracies and inefficiencies, can negatively impact employee satisfaction and trust. This study explores the integration of advanced technologies, specifically face recognition and attendance monitoring, into payroll management systems to address these challenges and enhance employee satisfaction.The research investigates the current state of employee satisfaction with existing compensation systems through a comprehensive survey conducted among employees from diverse industries. It identifies key factors contributing to dissatisfaction, such as payroll errors, delays, and perceived lack of transparency. The study then proposes an innovative payroll management system incorporating face recognition technology for accurate attendance tracking and automated payroll processing. Findings suggest that the integration of face recognition and attendance monitoring technology in payroll management systems can significantly enhance employee satisfaction by addressing common pain points and fostering a sense of fairness and transparency. By leveraging biometric data, the proposed system aims to ensure precise and real-time attendance records, thereby reducing payroll errors and administrative burdens. The face recognition technology offers a secure, contactless, and efficient method for attendance monitoring, aligning with modern workplace safety standards and employee preferences. The study concludes with recommendations for organizations considering the adoption of such technologies, emphasizing the importance of user training, data privacy, and continuous system evaluation to ensure long-term success.