Face Recognition Algorithm Research Articles

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Attendance Marking using Face Detection

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

Enhancing dance education through convolutional neural networks and blended learning

This article explores the evolving landscape of dance teaching, acknowledging the transformative impact of the internet and technology. With the emergence of online platforms, dance education is no longer confined to physical classrooms but can extend to virtual spaces, facilitating a more flexible and accessible learning experience. Blended learning, integrating traditional offline methods and online resources, offers a versatile approach that transcends geographical and temporal constraints. The article highlights the utilization of the dual-wing harmonium (DWH) multi-view metric learning (MVML) algorithm for facial emotion recognition, enhancing the assessment of students’ emotional expression in dance performances. Moreover, the integration of motion capture technology with convolutional neural networks (CNNs) facilitates a precise analysis of students’ dance movements, offering detailed feedback and recommendations for improvement. A holistic assessment of students’ performance is attained by combining the evaluation of emotional expression with the analysis of dance movements. Experimental findings support the efficacy of this approach, demonstrating high recognition accuracy and offering valuable insights into the effectiveness of dance teaching. By embracing technological advancements, this method introduces novel ideas and methodologies for objective evaluation in dance education, paving the way for enhanced learning outcomes and pedagogical practices in the future.

Some novel Dice similarity measures for picture fuzzy sets and their applications

The similarity measures, as an important tool for assessing the similarity between picture fuzzy sets, have attracted the attention of numerous scholars and achieved rich achievements. Although existing similarity measures have exhibited their unique research contributions, they still fail to distinguish between highly similar but different picture fuzzy sets. This phenomenon poses a major challenge in applications, limiting our ability to make accurate judgments in scenarios that necessitate meticulous differentiation of picture fuzzy sets. Furthermore, the current research focuses on proposing individual and specific calculation formulas, particularly in the realm of Dice similarity measures. However, the systematic, uniform construction of Dice similarity measures remains mostly untapped. The main objective of this article is to propose a method for constructing Dice similarity measures. Through this approach, we can not only integrate existing Dice similarity measures into a unified framework, but also generate novel similarity measures. Firstly, we introduce a function that satisfies specific conditions as the basis for constructing Dice similarity measures. Secondly, by selecting different functions, we obtain various similarity measures. Finally, we propose the pattern recognition algorithm, the face recognition algorithm and the cluster analysis algorithm, all based on the proposed similarity measures. These algorithms are then applied to various fields, including pattern recognition, face recognition, and clustering analysis. Experimental results demonstrate that the proposed similarity measures not only compensate for the shortcomings of existing similarity measures but also exhibit high reliability and practicality, offering a powerful new tool for research and applications in related fields.

Advancing Face Recognition Technologies: The Role of Decision Trees in Classifying Complex Image Pairs

The advancement of face recognition technologies has been pivotal in various applications, from security systems to personalized user experiences. There are significant efforts already devoted to solving challenges of multimodality and pose variation in face recognition. Some studies focus on multimodality but pose-invariant, and other studies focus on pose variation but single modality. Despite significant progress, various face recognition algorithms do not consider both multimodality and pose variation constraints in their proposed methods. Recognizing face images presented both in a different modality and in a different pose presents serious challenges to current algorithms. This paper proposes an algorithm that combines the strengths of deep learning with decision trees to improve face recognition performance across modalities and poses in constrained and unconstrained environments. This hybrid approach leverages the representational power of deep learning and the interpretability and simplicity of decision trees. The findings indicate significant improvements over existing methodologies, particularly in challenging conditions like when multimodality and pose variation constraints are compounded together in the input face images in both constrained and unconstrained environments. The proposed algorithm not only addresses the limitations of current face recognition systems but also offers scalable, efficient solutions suitable for real-world applications.

Training deep learning algorithms for face recognition with large datasets improves performance but reduces similarity to human representations

Training deep learning algorithms for face recognition with large datasets improves performance but reduces similarity to human representations

Comparative Analysis of FaceNet, VGGFace, and GhostFaceNets Face Recognition Algorithms For Potential Criminal Suspect Identification

The escalating concerns surrounding criminal activities underscore the imperative for bolstered security measures to safeguard public welfare. Despite concerted efforts, the identification of suspects remains fraught with limitations, hindering the attainment of comprehensive individual profiles. Leveraging advancements in facial detection and identification technologies, this study assesses the efficacy of three prominent deep learning models—FaceNet, VGGFace, and GhostFaceNets—in the domain of facial recognition for suspect identification. Drawing upon data collected in 2023, the investigation scrutinizes FaceNet's intricate methodologies, including triplet loss optimization and Euclidean space mappings, yielding exceptional accuracy rates of 97.05% during validation and 97.4% during testing. Conversely, VGGFace, while displaying commendable accuracies, registers marginally lower accuracy metrics, standing at 97.05% and 96.1% during validation and testing, respectively. GhostFaceNets, integrating novel architectural components, exhibit diminished accuracy rates, signaling avenues for refinement. These empirical insights underscore FaceNet's prowess in furnishing robust and reliable facial recognition outcomes, while delineating the imperative for iterative enhancements in GhostFaceNets to foster their pragmatic applicability in security domains.

Using geotagged facial expressions to visualize and characterize different demographic groups’ emotion in theme parks

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists’ emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups’ emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups’ emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists’ emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Are contemporary facial recognition algorithms making human facial comparison performance worse?

Facial recognition plays a vital role in several security and law enforcement workflows, such as passport control and criminal investigations. The identification process typically involves a facial recognition system comparing an image against a large database of faces to return a list of probable matches, called a candidate list, for review. A human then looks at the returned images to determine whether there is a match. Most evaluations of these systems tend to examine the performance of the algorithm or human in isolation, not accounting for the interaction that occurs in operational contexts. To ensure optimal whole system performance, it is important to understand how the output produced by an algorithm can impact human performance. Anecdotal claims have been made by users of facial recognition systems that the images being returned by new algorithms in these systems have become more similar in appearance compared to old algorithms, making their job of determining the presence of a match more difficult. This paper explores whether these claims are true and whether the latest facial recognition algorithms decrease human performance compared to an old algorithm from the same company. We examined the performance of 40 novice participants on 120 face matching trials. Each trial required the participant to compare a face image against a candidate list containing eight possible matches returned by either a new or old algorithm (60 trials of each). Overall, participants were more likely to make errors when presented with a candidate list from a new algorithm. Specifically, they were more likely to misidentify an incorrect identity as a match. Participants were more accurate, confident, and faster on candidate lists from the older algorithm. These findings suggest that new algorithms are generating more plausible matches, making the task of determining a match harder for humans. We propose strategies to potentially improve performance and recommendations for future research.

Research on facial expression recognition algorithm based on improved MobileNetV3

Aiming at the problem that face images are easily interfered by occlusion factors in uncontrollable environments, and the complex structure of traditional convolutional neural networks leads to low expression recognition rates, slow network convergence speed, and long network training time, an improved lightweight convolutional neural network is proposed for facial expression recognition algorithm. First, the dilation convolution is introduced into the shortcut connection of the inverted residual structure in the MobileNetV3 network to expand the receptive field of the convolution kernel and reduce the loss of expression features. Then, the channel attention mechanism SENet in the network is replaced by the two-dimensional (channel and spatial) attention mechanism SimAM introduced without parameters to reduce the network parameters. Finally, in the normalization operation, the Batch Normalization of the backbone network is replaced with Group Normalization, which is stable at various batch sizes, to reduce errors caused by processing small batches of data. Experimental results on RaFD, FER2013, and FER2013Plus face expression data sets show that the network reduces the training times while maintaining network accuracy, improves network convergence speed, and has good convergence effects.

A two-stage algorithm for heterogeneous face recognition using Deep Stacked PCA Descriptor (DSPD) and Coupled Discriminant Neighbourhood Embedding (CDNE)

A two-stage algorithm for heterogeneous face recognition using Deep Stacked PCA Descriptor (DSPD) and Coupled Discriminant Neighbourhood Embedding (CDNE)

Robust face recognition model based sample mining and loss functions

Traditional face recognition algorithms rely on margin-based softmax loss functions merely. However, these algorithms tend to perform poorly with low-quality images owing to the varying hardness of the datasets. To address this issue, we introduce a face recognition algorithm based on sample mining (FRABSM), an innovative face recognition algorithm that improves performance by integrating sample mining with conventional margin-based methods. Sample mining selectively focuses on specific samples during model training. FRABSM prioritises information-dense samples with more distinctive features. In this study, we present a probability-driven mining strategy that enhances the ability of the model to handle hard samples, thereby significantly increasing its robustness and adaptability. Mathematical evaluations demonstrate the effectiveness of FRABSM. An accuracy of 94.70% is achieved on the CPLFW. Additionally, the experimental results show that our approach achieves improvements over state-of-the-art methods on three renowned datasets (CPLFW, IJB-B, and TinyFace), highlighting its potential and efficiency. The source code is available at https://github.com/Xkf0/FRABSM.

DEVELOPMENT OF AN ELECTRONIC EXAMINATION PLATFORM USING FACE RECOGNITION METHODS

Online systems face a major challenge in efficiently monitoring participants and students throughout lectures, particularly during exams. Establishing robust approaches and technologies is essential for identifying unjust, unethical, and illegal conduct in educational environments, particularly during academic courses and examinations. This study introduces a novel online proctoring system that employs deep learning to continuously supervise physical locations without requiring a human proctor. The system utilizes face detection and recognition algorithms to implement biometric procedures, including facial recognition. This work presents a novel approach for face recognition training, which involves an incremental training procedure. This eliminates the requirement for an additional step, resulting in reduced computation cost and time. In order to achieve high accuracy, the suggested model evaluated three distinct face detectors: HOG, MTCNN, and Yoloface. The assessment of the suggested model demonstrates that the HOG approach has surpassed the others. The proposed approach yields a significantly high accuracy rate.

Emotional recognition and feedback of students in English e-learning based on computer vision and face recognition algorithms

In existing English teaching, teachers often find it difficult to accurately understand students’ emotional changes, thus unable to adjust teaching strategies in a timely manner. Therefore, the introduction of computer vision and facial recognition algorithms will help improve the effectiveness of English teaching. This article is based on computer vision technology and facial recognition algorithms. Based on the sample data provided during the learning process, a model is learned and established to recognize facial expressions under different emotions and identify students’ emotional states. Use computer vision technology to capture and analyze students’ facial expressions in real-time. Then, facial recognition algorithms are used to recognize and classify the captured facial features to determine the current emotional state of the students. Finally, based on the students’ emotional state, the system will provide corresponding feedback. This technology based on computer vision and facial recognition algorithms can help teachers better understand students’ emotional changes, adjust teaching strategies in a timely manner, provide personalized learning feedback, and thereby improve students’ learning effectiveness and the quality of English teaching. This technology can also provide students with a better learning experience, enhancing their learning motivation and interest.

HTNet for micro-expression recognition

Facial expression is related to facial muscle contractions and different muscle movements correspond to different emotional states. For micro-expression recognition, the muscle movements are usually subtle, which has a negative impact on the performance of current facial emotion recognition algorithms. Most existing methods use self-attention mechanisms to capture relationships between tokens in a sequence, but they do not take into account the inherent spatial relationships between facial landmarks. This can result in sub-optimal performance on micro-expression recognition tasks. Therefore, learning to recognize facial muscle movements is a key challenge in the area of micro-expression recognition. In this paper, we propose a Hierarchical Transformer Network (HTNet) to identify critical areas of facial muscle movement. HTNet includes two major components: a transformer layer that leverages the local temporal features and an aggregation layer that extracts local and global semantical facial features. Specifically, HTNet divides the face into four different facial areas: left lip area, left eye area, right eye area and right lip area. The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area. The aggregation layer is used to learn the interactions between eye areas and lip areas. The experiments on four publicly available micro-expression datasets show that the proposed approach outperforms previous methods by a large margin. The codes and models are available at: https://github.com/wangzhifengharrison/HTNet.

Identifying Missing Individual using AI

In recent days, the daily increase in many of the missing persons has made it increasingly challenging to locate them. To get over this challenge, machine learning algorithms, particularly facial recognition, can be employed to identify missing individuals. This approach aims to simplify the search process for both parents or guardians and the police. In this system, parents or guardians of the missing person upload a photo, which is stored in a database. The facial recognition algorithm then utilizes k-nearest neighbors (k-NN) to search for a match within the database. If a match is detected, both the police and the parents or guardians are alerted. Experimental outcomes tells that users appreciate the new features of the application and find the system user-friendly

Improved Facial Expression Recognition Algorithm Based on Local Feature Enhancement and Global Information Association

Facial expression recognition is the key area of research in computer vision, enabling intelligent devices to understand human emotions and intentions. However, recognition of facial expressions in natural scenes presents challenges due to environmental factors like occlusion and pose variations. To address this, we propose a novel approach that combines local feature enhancement and global information correlation. This method allows the model to learn both local and global facial features along with contextual information. By enhancing salient local features and exploring multi-scale facial expression features, our model effectively mitigates the impact of occlusion and pose variations, improving recognition accuracy. Experimental results demonstrate that our adapted model outperforms alternative algorithms in recognizing facial expressions under challenging environments, achieving recognition accuracies of 85.07% and 99.35% on the RAF-DB and CK+ datasets, respectively.

Deep Visual Computing of Behavioral Characteristics in Complex Scenarios and Embedded Object Recognition Applications.

By leveraging artificial intelligence and big data to analyze and assess classroom conditions, we can significantly enhance teaching quality. Nevertheless, numerous existing studies primarily concentrate on evaluating classroom conditions for student groups, often neglecting the need for personalized instructional support for individual students. To address this gap and provide a more focused analysis of individual students in the classroom environment, we implemented an embedded application design using face recognition technology and target detection algorithms. The Insightface face recognition algorithm was employed to identify students by constructing a classroom face dataset and training it; simultaneously, classroom behavioral data were collected and trained, utilizing the YOLOv5 algorithm to detect students' body regions and correlate them with their facial regions to identify students accurately. Subsequently, these modeling algorithms were deployed onto an embedded device, the Atlas 200 DK, for application development, enabling the recording of both overall classroom conditions and individual student behaviors. Test results show that the detection precision for various types of behaviors is above 0.67. The average false detection rate for face recognition is 41.5%. The developed embedded application can reliably detect student behavior in a classroom setting, identify students, and capture image sequences of body regions associated with negative behavior for better management. These data empower teachers to gain a deeper understanding of their students, which is crucial for enhancing teaching quality and addressing the individual needs of students.

Face Detection and Recognition for Criminal Identification System

This project showcases a powerful Face Detection and Recognition system intended to improve law enforcement skills by speeding up the identification of suspects in criminal cases. The system uses scalable databases and real-time computation, however instead of using traditional web sources for face detection. Accuracy and dependability are ensured by using the LBPH algorithm for face recognition and the Haar-cascade classifier for feature detection. The smtplib library allows the system to send a Gmail notification when it finds a matching face. The system that has been built contributes to public safety initiatives by improving law enforcement operations and demonstrating versatility for different face detection circumstances. The outcomes show how successfully the system can detect faces in real-time circumstances. The system achieves great performance by creating dynamic datasets and integrating algorithms. Integrating Gmail improves law enforcement response by facilitating prompt contact of identified suspects. This study highlights the role that surveillance technology plays in public safety and crime prevention, and it provides law enforcement authorities with useful solutions. Furthermore, the uses of this facial recognition and detection technology go beyond police enforcement. It can expedite attendance tracking and improve security in access control systems. Because of its adaptability, it can be used in a variety of industries and is expected to have a significant impact.