Facial Feature Extraction Research Articles

Human Face Emotion Recognition Using Deep Learning

Human Emotion detection from image is one of the most powerful and challenging research task in social communication. Deep learning based emotion detection gives performance better than traditional methods with image processing. This system is an artificial intelligence systemcapableofemotion detectionthrough facial expressions. It discusses about the procedure of emotion detection, which includes basically three main steps: face detection, features extraction, and emotion classification. This model proposed a convolutionalneural networks(CNN)and uses 3D face models based on deep learning architecture for emotion detection fromimages. Applicationsof face recognition detection extend across diverse fields and addressing challenges related to robustness, privacy, and ethical considerations is essential for the widespread and responsible adoption of face recognition technologyinthesefields.

Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures

The field of Digital Image Forensics (DIF) faces a critical issue in accurately identifying children in digital images, notably in cases involving the proliferation of child sexual abuse content. Existing techniques face hurdles due to model architecture limitations, dataset suitability concerns, and classification imbalance, impeding their ability to recognize children to deter pornographic images. Addressing this challenge, this study introduces Implicit Feature Extraction (IFE), a specialized approach for distinguishing child and adult images in object detection. Leveraging Convolutional Neural Networks (CNNs), the IFE method automates the extraction of discriminative facial features, surpassing the constraints of Explicit Feature Extraction (EFE) methods, which achieve an accuracy of around 70%. The research focuses on three core objectives introducing IFE for detailed face feature detection in DIF's child and adult image identification, implementing IFE with CNNs to enhance image classification, and conducting a thorough evaluation of the proposed technique's performance using key metrics like accuracy and balanced classification results and comparing the result with a basic CNN model’s performance. This research's significance lies in its notable contributions to digital image forensics, particularly in combatting child exploitation. The fusion of IFE with CNNs showcases 92% accuracy in distinguishing child and adult images, promising advancements with practical implications in child protection and forensic investigations. The comprehensive evaluation using the UTKFace dataset underscores the proposed technique's efficacy, marking a substantial improvement in child image identification within digital image forensics.

YOLO-FaceNet Fusion: Innovative System for Facial Recognition and Feature Extraction

Facial verification is vital in domains like Heathcare, Identity verification, etc. study presents a sophisticated facial verifying system utilizing state-of-the-art technologies: ArcFace is utilized for the edification of the model, while FaceNet is employed for the elicitation of facial characteristics. Initially, YOLO is deployed to discern and extricate visages from the input effigies. Subsequently, FaceNet is utilized to derive high-caliber facial attributes. In the final stage, ArcFace is employed for the model's edification, thus augmenting its robustness and precision. The apparatus gauges facial resemblance employing the Euclidean distance metric and is appraised through measures such as recall, accuracy, and precision. This approach yields a dependable and accurate facial verification system, thereby enhancing operational efficiency in practical applications.. Keywords – You Only Look Once (YOLO), ArcFace, Convolutional Neural Network (CNN), FaceNet, Machine Learning, Deep Learning, Euclidean Distance.

JGULF: Joint global and unilateral local feature network for micro-expression recognition

Micro-expression is a subtle facial movement that is fleeting and manifest in localized areas, making it difficult for the human eye to detect and recognize it. Although algorithms that extract facial features from specific regions or the entire face have shown potential, the classification of micro-expressions using features from symmetrical left and right regions can be challenging in the presence of unilateral movements. This can ultimately affect the performance of micro-expression recognition. To address this issue, we propose a network called Joint Global and Unilateral Local Features (JGULF) for micro-expression recognition. Initially, we employ a Convolutional Neural Network (CNN) and an adjusted Vision Transformer (ViT) model to extract global features from micro-expressions. The local feature extraction module is designed based on global features. The facial features are divided into multiple local regions with varying scales. After that, local feature learning and selection are performed to filter out unilateral local features related to micro-expression movements efficiently. Finally, global and local features are combined to classify micro-expressions. Through comprehensive experimental validation, our algorithm achieves state-of-the-art classification performance on the SMIC, CASMEII, and SAMM micro-expression datasets, demonstrating the effectiveness of combining global features and selecting local features.

JeFaPaTo - A joint toolbox for blinking analysis and facial features extraction

JeFaPaTo - A joint toolbox for blinking analysis and facial features extraction

Optimizing facial feature extraction and localization using YOLOv5: An empirical analysis of backbone architectures with data augmentation for precise facial region detection

Optimizing facial feature extraction and localization using YOLOv5: An empirical analysis of backbone architectures with data augmentation for precise facial region detection

FrseGAN: Free‐style editable facial makeup transfer based on GAN combined with transformer

AbstractMakeup in real life varies widely and is personalized, presenting a key challenge in makeup transfer. Most previous makeup transfer techniques divide the face into distinct regions for color transfer, frequently neglecting details like eyeshadow and facial contours. Given the successful advancements of Transformers in various visual tasks, we believe that this technology holds large potential in addressing pose, expression, and occlusion differences. To explore this, we propose novel pipeline which combines well‐designed Convolutional Neural Network with Transformer to leverage the advantages of both networks for high‐quality facial makeup transfer. This enables hierarchical extraction of both local and global facial features, facilitating the encoding of facial attributes into pyramid feature maps. Furthermore, a Low‐Frequency Information Fusion Module is proposed to address the problem of large pose and expression variations which exist between the source and reference faces by extracting makeup features from the reference and adapting them to the source. Experiments demonstrate that our method produces makeup faces that are visually more detailed and realistic, yielding superior results.

Finding Missing Person Using Face Recognition

Abstract: This project presents a novel approach to enhance the efficiency and effectiveness of locating missing persons through the utilization of face recognition technology The main objective is to develop a robust system that can extract facial features, match them with a database, and notify the missing person's location. The system uses a special type of artificial intelligence called CNN to compare faces with the database and find the best matches. The face recognition system uses the CNN (Convolutional Neural Network) algorithm to compare the extracted faces with the database and find the best matches. The alert module sends notifications to the family members of the missing person with the location information if a match is found. The system also updates the database with the latest information about the missing person. The project leverages state-of-the-art machine learning algorithms to extract and compare facial features, it reduces the effort and time required for manual process. This project aims to find missing people faster and better by using facial recognition technology. The main goal is to build a strong system that can study facial details, match them with a database of known people, and help to find missing people quickly and correctly. By harnessing the power of artificial intelligence, this system offers a promising solution to the pressing issue of finding missing individuals, potentially helping to reunite families and improve public safety.

AI based Face Recognition Attendance System

The AI Based Face Recognition Attendance System represents an innovative solution at the intersection of artificial intelligence and attendance management. Leveraging Python open-source libraries such as OpenCV and NumPy, alongside machine learning techniques, the system aims to streamline the attendance tracking process. By employing face detection algorithms and feature extraction methods, the system identifies individuals, records their attendance, and updates the database in real-time. The system architecture incorporates components like a frontend web application, real-time prediction module, registration form module, reporting module, Redis database, Streamlit framework, Insightface library, and AWS deployment. Through continuous improvement and innovation, the system enhances user experience, accuracy, and efficiency in attendance management.

Detection and Classification of Human Gender into Binary (Male and Female) Using Convolutional Neural Network (CNN) Model

This paper focuses on detecting the human gender using Convolutional Neural Network (CNN). Using CNN, a deep learning technique used as a feature extractor that takes input photos and gives values to various characteristics of the image and differentiates between them, the goal is to create and develop a real-time gender detection model. The model focuses on classifying human gender only into two different categories; male and female. The major reason why this work was carried out is to solve the problem of imposture. A CNN model was developed to extract facial features such as eyebrows, cheek bone, lip, nose shape and expressions to classify them into male and female gender, and also use demographic classification analysis to study and detect the facial expression. We implemented both machine learning algorithms and image processing techniques, and the Kaggle dataset showed encouraging results.

Student's Emotion Recognition using Multimodality and Deep Learning

The goal of emotion detection is to find and recognise emotions in text, speech, gestures, facial expressions, and more. This paper proposes an effective multimodal emotion recognition system based on facial expressions, sentence-level text, and voice. Using public datasets, we examine face expression image classification and feature extraction. The Tri-modal fusion is used to integrate the findings and to provide the final emotion. The proposed method has been verified in classroom students, and the feelings correlate with their performance. This method categorizes students' expressions into seven emotions: happy, surprise, sad, fear, disgust, anger, and contempt. Compared to the unimodal models, the suggested multimodal network design may reach up to 65% accuracy. The proposed method can detect negative feelings such as boredom or loss of interest in the learning environment.

Face Emotional Detection Using Neural Network

The face reaction detection using neural network is a very much popular topic in the artificial intelligence and compute vision field. The main goal of this research is to develop a system that can accurately detect the emotional system of a person based on these facial expression. The proposed system consists of so many stages, including face detection, feature extraction, and classification. In the first stage, the face is detected and cropped from the input image using a pre-trained face detection model. Then, the relevant facial features are, like, extracted, such as the position and shape of the eyebrows, mouth, and eyes, and other stuff. The extracted features are then fed into a neural network model, which is trained on a large dataset of labeled facial expressions to learn the relationship between the facial features and emotional states. To evaluate the performance of the proposed system, several metrics are used, like, accuracy, precision, recall, and F1-score. The system is tested on a large dataset of images with labeled emotional states, and the results show that, woah, the system achieves high accuracy and precision in detecting emotions. In conclusion, the proposed system is, you know, an effective approach for face emotional detection using neural networks. The system can be used in a variety of applications, such as human computer interaction and social robotics and emotion-based marketing, all those cool things.

Realtime Facial Emotion Detection

Abstract: A crucial component of communication is facial emotion through facial expression. This makes the problem of evaluating human emotions via computer systems an intriguing one that has attracted increasing amounts of attention in the past few decades. The main connection is that facial expression recognition could be used in a variety of industries, including virtual reality, video games, HCI, and customer satisfaction analysis. Face detection, facial feature extraction, and expression categorization are the final three basic steps in the emotions determination (recognition process). The Russell circular approach, which has up to 24 emotional expressions, and Plutchik's Wheel of Emotions are two other categorization schemes that are frequently encountered. Ekman's classification, which has six emotional expressions (or seven, if it is a neutral expression), is the most common.

Beyond the Mask: Advancements in Facial Recognition through Convolutional Neural Networks

Abstract: Facial recognition technology has gained immense popularity in recent years, yet challenges persist with face alterations and the widespread use of masks. Uncooperative individuals in real-world scenarios, such as video surveillance, often resort to masking, leading to a degradation in current face recognition performance. While extensive research has addressed face recognition under various conditions, the impact of masks has often been overlooked. This research focuses specifically on improving the accuracy of recognizing faces obscured by different masks. Our proposed approach involves initial detection of facial regions, addressing the occluded face detection problem through the Multi-Task Cascaded Convolutional Neural Network (MTCNN). Subsequently, facial feature extraction utilizes the Google Face Net embedding model, followed by a classification task executed by Support Vector Machine (SVM). Experimental results demonstrate the effectiveness of this approach in masked face recognition, with noteworthy performance even under excessive facial masks. Additionally, a comparative study enhances our understanding of the proposed method's capabilities

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Unplugged K-12 AI Learning: Exploring Representation and Reasoning with a Facial Recognition Game

With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

X2-Softmax: Margin adaptive loss function for face recognition

Learning the discriminative features of different faces is an important task in face recognition. By extracting face features with neural networks, it becomes easy to measure the similarity of different face images, which makes face recognition possible. To enhance a neural network’s face feature separability, incorporating an angular margin during training is common practice. The state-of-the-art loss functions CosFace and ArcFace apply fixed margins between the weights of classes to enhance the inter-class separation of face features. Since the distribution of samples in the training set is uneven, the similarities between different identities are unequal. Therefore, using an inappropriately fixed angular margin may lead to problems such as that the model has difficulty converging or that the face features are not sufficiently discriminative. It is more intuitive to use adaptive angular margins are angular adaptive, which can increase as the angles between classes increase. In this paper, we propose a new angular margin loss named X2-Softmax. X2-Softmax loss has adaptive angular margins, which increase as the angle between different classes increases. The angular adaptive margin ensures model flexibility and effectively improves the effect of face recognition. We trained a neural network with X2-Softmax loss on the MS1Mv3 dataset and tested it on several evaluation benchmarks to demonstrate the effectiveness and superiority of our loss function.

Facial emotion recognition with a reduced feature set for video game and metaverse avatars

AbstractThis paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. The algorithm aims to reduce data transmission and storage requirements, hurdles in the adoption of emotion recognition in these mediums. The early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research.

Domain adaptation for driver's gaze mapping for different drivers and new environments

Distracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has shown promising results, but it faces challenges when applied to different drivers and environments. In this paper, we propose a domain adaptation-based solution for gaze mapping, which aims to accurately estimate a driver's gaze in diverse drivers and new environments. Our method consists of three steps: pre-processing, facial feature extraction, and gaze region classification. We explore two strategies for input feature extraction, one utilizing the full appearance of the driver and environment and the other focusing on the driver's face. Through unsupervised domain adaptation, we align the feature distributions of the source and target domains using a conditional Generative Adversarial Network (GAN). We conduct experiments on the Driver Gaze Mapping (DGM) dataset and the Columbia Cave-DB dataset to evaluate the performance of our method. The results demonstrate that our proposed method reduces the gaze mapping error, achieves better performance on different drivers and camera positions, and outperforms existing methods. We achieved an average Strictly Correct Estimation Rate (SCER) accuracy of 81.38% and 93.53% and Loosely Correct Estimation Rate (LCER) accuracy of 96.69% and 98.9% for the two strategies, respectively, indicating the effectiveness of our approach in adapting to different domains and camera positions. Our study contributes to the advancement of gaze mapping techniques and provides insights for improving driver safety in various driving scenarios.