Face Recognition Accuracy Research Articles

Drone-Based Adaptive Hybrid Techniques for Improving Face Detection and Recognition

With the rapid development of Unmanned Aerial Vehicle (UAV) related technologies, Drones gained the opportunity to search locations that are dangerous or difficult for humans to reach. In this regard, Drones technology has been widely used to detect and recognize humans on the ground. In order to identify their potential role in counter-terrorism operations, the aim of this research study is to design and develop a reliable aerial system capable of identifying people with a high detection rate. Furthermore, it presents a solution to all challenges that degrade the resolution when query images are taken from high altitudes, different angles, and long distances. In the context of improving the accuracy of facial detection and recognition, two adaptive techniques are proposed: The first approach is to adaptively adjust the Drone’s altitude, speed, and attitude based on two parameters: Ground sampling distance (GSD), and confidence level of the recognized face. The second approach is to adaptively adjust the resolution of images captured by drone based on the size of the detected facial area. Face detection and recognition are done using the Haar Cascade classifier and Local Binary Pattern Histogram (LBPH) algorithm. The entire system was developed, implemented, and tested using a Hexacopter Drone and onboard vision system. The testing results corroborate the system's practicality and demonstrate that the prototype may be simply implemented to track dangerous people with criminal records.

A Research on Two-Stage Facial Occlusion Recognition Algorithm based on CNN

In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.

Application of big data and artificial intelligence in visual communication art design.

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications' reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome

Classifying the faces of individuals with Down Syndrome poses a significant challenge in image processing and genetic anomaly detection. This study leverages the ResNet34 architecture and transfer learning methods to improve classification accuracy for Down Syndrome facial recognition. Three experiments were conducted, varying the batch size, learning rate, and number of epochs. In the first experiment, the model achieved an accuracy of 82.83%, precision of 0.8362, recall of 0.8350, and an F1 score of 0.8348, showing promising performance but falling short of the target accuracy of 85%. The second experiment yielded the best results, with an accuracy of 87.88%, precision of 0.8956, recall of 0.8956, and an F1 score of 0.8956, indicating an optimal balance between correct predictions and errors. The third experiment resulted in the lowest accuracy, at 80.47%, with a precision of 0.8272, recall of 0.8249, and an F1 score of 0.8247, signifying a decline in performance compared to the other trials. Among the three experiments, the best configuration was achieved in the second trial, as the high recall value is crucial in medical contexts to ensure that as many individuals with Down Syndrome are correctly detected as possible, minimizing the risk of serious consequences due to false negatives.

Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition

The study focuses on using vitality detection and deep learning technologies in the context of facial recognition in an IT presence management project. The combination of deep learning with vitality detection provides a considerable advancement in security and effectiveness. This work integrated vitality-detecting technology with in-depth learning in facial recognition systems. Vitality detection technologies are used to verify the authenticity of persons by examining live indicators such as movements or facial expressions before face recognition. Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. Model testing and training are carried out in the Google Colab environment, using Python and the Hardy package. The test findings showed an 87% accuracy in face recognition, proving the system's capacity to consistently identify persons and distinguish real from false ones. Furthermore, the performance of Liveness Detection achieves 92% accuracy, as does the integration of Live Detection technology with Deep Learning at 78%.

Home Door Security System with Face Recognition Using ESP 32 Cam

The development of science and technology, especially in the fields of Electronics and Informatics, is expected to enhance room or home security systems, particularly home door security systems. Many door security technologies have been developed to prevent and ensure the comfort of homeowners when they leave their homes. Some commonly used door security systems include CCTV cameras, fingerprint sensors, voice sensors, and RFID. However, with technological advancements, these security systems have shown vulnerabilities, such as frequent errors in fingerprint and voice sensors. Based on this, to create a more secure and innovative home door security system, the authors developed a security system with face detection and a remote warning notification system using Telegram. The researchers created a door security system using the ESP 32-CAM for face detection and as a controller for the Internet of Things (IoT) system. Additionally, a manual system using a keypad for password input was also created. The results of this study include the accuracy and speed of face recognition and the speed of sending security system warning information through the Telegram application. Keywords— Door Lock System, Face Recognition, ESP32 Camera, Microcontroller, Internet of Things.

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

APLIKASI PENGENALAN WAJAH UNTUK ABSENSI PADA SMA DUA MEI CIPUTAT MENGGUNAKAN METODE HAAR-CASCADE CLASSIFIER BERBASIS WEBSITE

The attendance application is one important system that must be possessed by a school in order to manage student attendance data. Some schools still use manual methods for student attendance by using signatures or calling them one by one. Manual attendance is considered inefficient because the risk of losing attendance sheets is very easy and parents cannot monitor whether their child is attending school or not. This is directly felt by SMA Dua Mei Ciputat with the presence of students who left home but did not attend school. Therefore, this research aims to assist teachers and parents in monitoring students to be more disciplined in coming to school by building a face recognition application for attendance using the haar-cascade classifier method based on a website. The haar-cascade classifier method has the advantages of computation in face detection and the local binary pattern histogram algorithm which has good accuracy in face recognition. The results of this research show that the haar-cascade classifier method can be used to create a face recognition application for attendance with fairly good accuracy, namely 100% with 5 attempts with recognition of one face, 100% with 5 attempts with recognition of two faces, and 20% with 5 attempts with recognition of three faces. With these results, the application is only capable of recognizing two faces simultaneously in the attendance process.

Face Recognition-Based Door Lock Security System Using TensorFlow Lite

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies

Direct implementation of AI-Based Facial Recognition for ITSI students

The development of artificial intelligence (AI)-based facial recognition technology has become a significant research topic in the field of computing and security. At the Indonesian Palm Oil Institute (ITSI), AI-based facial recognition is introduced to students to improve their skills in developing AI-based applications. This study aims to implement and test a facial recognition system using a Python program by utilizing a dataset generated independently. This research method involves several stages, namely collecting ITSI students' facial data, data processing, creating a facial recognition model using a machine learning algorithm, and evaluating model performance. The dataset used was developed through a live shooting session involving active student participation. The facial recognition model was trained using a convolutional neural network (CNN) algorithm that was optimized to improve accuracy. The results of the study showed that the developed model was able to achieve high facial recognition accuracy, with an average accuracy rate of 92%. The discussion includes an analysis of factors that affect accuracy, such as variations in lighting and shooting angles, as well as the potential use of this technology in a campus environment, including for attendance and security purposes. The conclusion of this study shows that the implementation of AI-based facial recognition can be effectively applied in an academic environment, as well as providing students with practical experience in developing and testing AI applications. This study also opens up opportunities for further research on improving the performance of facial recognition systems and their application in various real-world scenarios.

Face Recognition Attendance Management System using LBPH and Haar Cascade

This research presents the implementation and design of a facial recognition attendance management system by using Local Binary Pattern Histogram (LBPH) and Haar Cascade machine learning algorithms. This system aims to automate the attendance process, providing an efficient and accurate alternative to traditional methods. The Haar Cascade algorithm is employed for face detection due to its rapid processing and high detection rate, while the LBPH algorithm is utilized for face recognition because it is simple and effective in handling changes in facial expressions, lighting, occlusions, distance from the camera, and camera resolution. The integration of these algorithms results in a robust system capable of real-time attendance tracking. Experimental results demonstrate the system's high accuracy and reliability in face detection and recognition under different conditions of lighting, distance from the camera, face expressions, occlusions, and camera resolution, making it suitable for deployment in educational institutions and corporate environments.

A facial expression recognition network based on attention double branch enhanced fusion.

The facial expression reflects a person's emotion, cognition, and even physiological or mental state to a large extent. It has important application value in medical treatment, business, criminal investigation, education, and human-computer interaction. Automatic facial expression recognition technology has become an important research topic in computer vision. To solve the problems of insufficient feature extraction, loss of local key information, and low accuracy in facial expression recognition, this article proposes a facial expression recognition network based on attention double branch enhanced fusion. Two parallel branches are used to capture global enhancement features and local attention semantics respectively, and the fusion and complementarity of global and local information is realized through decision-level fusion. The experimental results show that the features extracted by the network are made more complete by fusing and enhancing the global and local features. The proposed method achieves 89.41% and 88.84% expression recognition accuracy on the natural scene face expression datasets RAF-DB and FERPlus, respectively, which is an excellent performance compared with many current methods and demonstrates the effectiveness and superiority of the proposed network model.

Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.

Epilepsy patients rank memory problems as their most significant cognitive comorbidity. Current clinical assessments are laborious to administer and score and may not always detect subtle memory decline. The Famous Faces Task (FF) has robustly demonstrated that left temporal lobe epilepsy (LTLE) patients remember fewer names and biographical details compared to right TLE (RTLE) patients and healthy controls (HCs). We adapted the FF task to capture subjects' entire spontaneous spoken recall, then scored responses using manual and natural language processing (NLP) methods. We expected to replicate previous group level differences using spontaneous speech and semi-automated analysis. Seventy-three (N=73) adults (28 LTLE, 18 RTLE, and 27 HCs) were included in a case-control prospective study design. Twenty FF in politics, sports, and entertainment (active 2008-2017) were shown to subjects, who were asked if they could recognize and spontaneously recall as much biographical detail as possible. We created human-generated and automatically-generated keyword dictionaries for each celebrity, based on a randomly selected training set of half of the HC transcripts. To control for speech output, we measured the speech duration, total word count and content word count for the FF task and a Cookie Theft Control Task (CTT), in which subjects were merely asked to describe a visual scene. Subjects' responses to FF and CTT tasks were recorded, transcribed, and analyzed in a blinded manner with a combination of manual and automated NLP approaches. Famous face recognition accuracy was similar between groups. LTLE patients recalled fewer biographical details compared to HCs and RTLEs using both the gold-standard human-generated dictionary (24%±12% vs. 31%±12% and 30%±12%, p=0.007) and the automated dictionary (24%±12% vs. 31%±12% and 32%±13%, p=0.007). There were no group level differences in speech duration, total word count, or content word count for either the FF and CTT to explain difference in recall performance. There was a positive, statistically significant relationship between MOCA score and FF recall performance as scored by the human-generated (ρ= .327, p= .029) and automatically-generated dictionaries (ρ= .422, p= .004) for TLE subjects, but not HCs, an effect that was driven by LTLE subjects. LTLE patients remember fewer details of famous people than HCs or RTLE patients, as discovered by NLP analysis of spontaneous recall. Decreased biographical memory was not due to decreased speech output and correlated with lower MOCA scores. NLP analysis of spontaneous recall can detect memory dysfunction in clinical populations in a semi-automated, objective, and sensitive manner.

Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern

Enhancing Facial Recognition Accuracy through KNN Classification with Principal Component Analysis and Local Binary Pattern

Facial length and angle feature recognition for digital libraries.

With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries.

Smart reception: An artificial intelligence driven bangla language based receptionist system employing speech, speaker, and face recognition for automating reception services

In recent times, service robots (SR) have become widely accepted in a variety of fields as an alternative to traditional reception methods. Artificial Intelligence (AI) driven systems are seen as efficient labor alternatives, resulting in several SR models already being developed, primarily designed for English-speaking environments. Despite a large Bangla-speaking population, the exploration and implementation of Bangla language support within AI reception systems remain unexplored due to limited resources, posing significant challenges for deployment. This study presents a novel AI-enabled receptionist framework tailored to Bangla-speaking environments, addressing the limitation of Bangla language resources for developing automated reception systems. Leveraging advanced AI technologies including Face Recognition, Speaker Recognition, Automatic Speech Recognition (ASR), Text-to-Speech Synthesis, and Question Answering System, our integrated system demonstrates promise to automate Bangla reception systems. Our suite of models yields positive performance, with a face recognition accuracy of 99.38%, a speaker recognition system with 5.83 Equal Error Rate (ERR), ASR model achieving a Word Error Rate (WER) of 9.005%, TTS model scoring a Mean Opinion Score (MOS) of 4.10, and a question-answering system with a validation loss of 0.03%. Real-world evaluation among 1664 users in a university setting achieved over 75% user satisfaction, with 88% expressing interest in real-life implementation, showcasing usability across different domains. Limitations such as limited training data, scalability, and environmental sensitivity persist, underscoring the need for further development. Nevertheless, our framework demonstrates potential for real-life implementation, fostering human-robot interaction in Bangla-speaking contexts and paving the way for future innovations in AI-driven Bangla receptionist systems.

Perbandingan Dataset Labelled Faces in the Wild (LFW) dan faces94 Menggunakan Algoritma Convolutional Neural Networks (CNN) untuk Pengenalan Wajah

This research compares the performance of two popular datasets, Labelled Faces in the Wild (LFW) and faces94, in the task of face recognition using Convolutional Neural Networks (CNN) algorithms. The LFW dataset is known for its high variation in pose, lighting, and expression, while faces94 is more structured with more uniform lighting and pose conditions. CNNs were chosen for their ability to extract important features from face images for classification. In this study, a CNN model was trained on both datasets and its performance was evaluated using accuracy, precision, and recall metrics. The experimental results showed that the model trained on the faces94 dataset achieved higher accuracy compared to the model trained on the LFW dataset. However, the model on the LFW dataset demonstrated better resilience to variations in lighting and pose conditions. These findings indicate that while a more structured dataset like faces94 can produce a model with high accuracy under testing conditions similar to the training data, a dataset with greater variation like LFW is more suitable for real-world applications involving diverse conditions. This study provides important insights into the selection of datasets for developing robust and accurate face recognition systems.

Ethics-aware face recognition aided by synthetic face images

Current face recognition models trained on large-scale face datasets have achieved promising performance. However, using face images to train a face recognition model without consent would lead to severe privacy and ethical issues. Moreover, existing face recognition models also exhibit uneven performance on different races, thus perplexing vulnerable populations. To address the aforementioned two issues, this work investigates an ethics-aware face recognition method and examines whether we can leverage synthesized faces to achieve a high-accuracy racial balanced recognition model. In a nutshell, we introduce a race-controllable and identity-innumerable face synthesis approach to generate synthetic face images, and then employ the synthesized images to improve face recognition accuracy and mitigate recognition imbalance among different races despite the scarcity of consenting images (less than 100 individuals). More importantly, the synthetic data enable us to analyze the potential impacts of races on face recognition models quantitatively and facilitate the eradication of racial imbalance in face recognition. Extensive experiments demonstrate that employing our synthetic face data improves face recognition accuracy by a large margin while mitigating the recognition imbalance across different race groups.

Face Recognition Using LBPH and CNN

Objective:: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods:: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results:: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion:: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications.

Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.