Facial Feature Detection Research Articles

Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction.

Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

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.

Advancements in driver fatigue detection: A comprehensive analysis of eye movement and facial feature approaches

This article goes into sophisticated tiredness detection methods that make use of computer vision algorithms, including the Eye Movement and Facial Feature approaches. Eye movement analysis, which emphasizes blink frequency, Scanning speed, and gaze length, excels in tasks requiring sustained visual attention, which is critical in high-attention vocations. Facial Feature detection, on the other hand, monitors changes in expressions, muscle activity, and emotions, providing flexibility across a wide range of settings. Eye movement enables accurate indications, real-time responsiveness, and task-specific precision, which is especially important in high-attention occupations. Facial Feature is ideal for a variety of scenarios since it gives complete assessments, flexibility, contextual analysis, and non-intrusiveness. Case examples show how eye-tracking and convolutional neural networks may be used to improve accuracy. This study helps to weariness management by detailing the advantages and disadvantages of Eye Movement and Facial Feature methods. Given the pervasiveness of fatigue, knowing these sensing systems is critical for maintaining safety, productivity, and general well-being in a variety of professional and everyday life scenarios.

VigilEye: Machine learning-powered driver fatigue recognition for safer roads

In an era where autonomous vehicles are on the horizon, the importance of human vigilance during driving cannot be understated. One of the paramount challenges road safety advocates face is driver fatigue, a silent culprit behind many tragic accidents. Our project seeks to address this issue by merging facial feature recognition with cutting-edge machine learning techniques, harnessing tools such as OpenCV and Dlib. This approach is centred around 68 precise facial feature detectors, adept at capturing specific markers like the status of a driver's eyes. Once data is acquired, our algorithms scrutinize it for fatigue indicators. Offering both cost and user benefits, our non-intrusive system swiftly alerts drivers, through auditory or tactile means, upon detecting drowsiness. Our system achieved a remarkable 94 % efficiency in timely and accurate fatigue detection through exhaustive testing across varied scenarios, underscoring its potential to revolutionize road safety.

Movie and Music Recommendation System based on Facial Expressions

Abstract: This paper depicts a novel approach of recommendation system created to extract user’s facial expressions and analyze it using Convolutional Neural Network. CNN is a type of artificial neural network which primarily is used for image recognition and processing by recognizing patterns in images. Initially, an inbuilt webcam will operate to detect user’s facial expression which reduces system’s cost compared to alternative methods. The main concern in existing recommendation systems is manual sorting, so to avoid it, this model is proposed. ‘Movie and Music Recommendation System based on Facial expressions’ provides a way to automatically play the music and movie without spending much time browsing it. The most common problem we all face is, whenever we hop onto third party movies and music platforms, we find tons of options and get stuck into the confusion of what to watch in terms of movies and spend half of our time surfing the options and then we decide to watch something specific. Computer vision is used to understand people in images and videos. This research project introduces an ingenious recommendation system that capitalizes on facial expressions in real time, customize movie and music suggestions through emotion analysis. Haar Cascade algorithm and CNN targets to detect facial feature detection. The database of movies is routed to IMDB site and songs to Spotify API. Training of dataset is done on FER 2013 sample images. It has an input layer, convolutional layer, dense layer and output layer. After the emotions are detected, mood classification is done (for song: tone, valence, timber; for movie: happy, surprise, sad, neutral, anger.) The model is displayed on a website created using Html and CSS with Flask as interface. This system yields better performance results. This recommendation system is exhibited on a website which is user friendly created with HTML and CSS with Flask.

Real-Time Noise Suppression Method Using Deep Learning Algorithm

In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (MultiTask Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.

Intelligent Assessment and Feedback: Managing Student Learning States in Industrial Education

Abstract Students are the main body of the classroom, and their learning status reflects the quality of classroom teaching to a certain extent. This paper aims to manage students’ learning status in industrial education classrooms by designing a student learning status assessment system. The collected video data from industrial education classrooms are processed by binarization and histogram equalization. The key technologies, such as face detection and eye movement analysis, are used to detect the learning status of students in the industrial education classroom, and the functional modules, such as data acquisition and statistical analysis, are combined to form this paper’s student learning status assessment system based on facial feature detection. It is found that the face detection algorithm of this paper’s system improves the detection accuracy by 11.35% compared with the baseline algorithm when the standard difficulty is difficult, and this paper’s algorithm is able to successfully detect and display the students’ facial features through 68 key feature points. The system in this paper is able to detect students’ learning states, such as concentration, fatigue, and doubt, by analyzing their eye movement frequency and other indicators. After applying the system discussed in this paper, the final grades of students in industrial education have significantly improved.

Enhancing polarization 3D facial imaging: overcoming azimuth ambiguity without extra depth devices.

Polarization 3D imaging has been a research hotspot in the field of 3D facial reconstruction because of its biosafety, high efficiency, and simplicity. However, the application of this technology is limited by the multi-valued problem of the azimuth angle of the normal vector. Currently, the most common method to overcome this limitation is to introduce additional depth techniques at the cost of reducing its applicability. This study presents a passive 3D polarization facial imaging method that does not require additional depth-capturing devices. It addresses the issue of azimuth ambiguity based on prior information about the target image's features. Specifically, by statistically analyzing the probability distribution of real azimuth angles, it is found that their quadrant distribution is closely related to the positions of facial feature points. Therefore, through facial feature detection, the polarized normal azimuth angle of each pixel can be accurately assigned to the corresponding quadrant, thus determining a precise unique normal vector and achieving accurate 3D facial reconstruction. Finally, our azimuth angle correction method was validated by simulated polarization imaging results, and it achieved accurate correction for over 75% of the global pixels without using additional depth techniques. Experimental results further indicate that this method can achieve polarization 3D facial imaging under natural conditions without extra depth devices, and the 3D results preserve edge details and texture information.

A Novel Approach for Human Face Extraction and Detection using SAE-AFB-RFCN Framework

Research into Facial Recognition Technology (FRT), which uses a person's face to identify them, has become a hot topic among scientists. Face recognition relies heavily on feature extraction and classifiers. Occlusion, illuminations, and a complicated background provide the most difficult problems for face recognition systems to overcome. With the advent of Artificial Intelligence and Deep Learning techniques now it became easy to identify different features of an Image and to detect a face. In this paper, Stacked Auto Encoder (SAE), Artificial Feeding Bird (AFB), Region Based Fully Convolutional Network (RFCN), novel approach is developed for human face feature extraction and detection. Initially, the dataset is normalized using rescaling method. Then the Stacked Auto Encoder with Artificial Feeding Birds (SAE-AFB) optimization algorithm is used for facial feature extraction and Region based Fully Convolutional Networks (R-FCN) algorithm is used for detection and classification. The WIDER Face dataset is used for training and testing. Experimental results demonstrate that the proposed SAE-AFB-RFCN framework outperforms the existing algorithms in terms of accuracy, precision, recall and F1-score.

Child Trafficking Mitigating System using Transfer Learning and Modified Deep Learning

In India, the National Human Rights Commission said that 11,000 of the 40,000 kidnapped children go unrecoverable each year. This project suggests a method for creating a useful app to locate a missing person by correlating the database and the identification feed. To check if the suspected child is a victim of child trafficking and is listed as a missing child, the person can scan their image. A facial recognition system has been employed which uses CNN (Convolution Neural Network ) a deep learning technology for digital image processing and to identify or confirm a person. Current world scenarios and technology make substantial use of facial feature detection and recognition but here we have also introduced alert system The problems of the modern world, on the other hand, have remedies thanks to artificial intelligence. To improve communication between humans and machines, artificial intelligence (AI) has been developed. Convolution neural networks and deep learning will be heavily utilised to analyse images and make appropriate inferences as part of the implementation of the aforementioned solution. By sending the victim's position information and photo to the closest police station, the user initiates the rescue effort and minimises the area that needs to be searched for the youngster.

Retracted: Facial Features Detection System to Identify Children with Autism Spectrum Disorder: Deep Learning Models.

[This retracts the article DOI: 10.1155/2022/3941049.].

Face Feature Detection Based on Structured Light Projection Three-Dimensional Reconstruction

Face Feature Detection Based on Structured Light Projection Three-Dimensional Reconstruction

Affine Layer-Enabled Transfer Learning for Eye Tracking with Facial Feature Detection in Human–Machine Interactions

Eye tracking is an important technique for realizing safe and efficient human–machine interaction. This study proposes a facial-based eye tracking system that only relies on a non-intrusive, low-cost web camera by leveraging a data-driven approach. To address the challenge of rapid deployment to a new scenario and reduce the workload of the data collection, this study proposes an efficient transfer learning approach that includes a novel affine layer to bridge the gap between the source domain and the target domain to improve the transfer learning performance. Furthermore, a calibration technique is also introduced in this study for model performance optimization. To verify the proposed approach, a series of comparative experiments are conducted on a designed experimental platform to evaluate the effects of various transfer learning strategies, the proposed affine layer module, and the calibration technique. The experiment results showed that the proposed affine layer can improve the model’s performance by 7% (without calibration) and 4% (with calibration), and the proposed approach can achieve state-of-the-art performance when compared to the others.

Transformer-Based GAN for New Hairstyle Generative Networks

Traditional GAN-based image generation networks cannot accurately and naturally fuse surrounding features in local image generation tasks, especially in hairstyle generation tasks. To this end, we propose a novel transformer-based GAN for new hairstyle generation networks. The network framework comprises two modules: Face segmentation (F) and Transformer Generative Hairstyle (TGH) modules. The F module is used for the detection of facial and hairstyle features and the extraction of global feature masks and facial feature maps. In the TGH module, we design a transformer-based GAN to generate hairstyles and fix the details of the fusion part of faces and hairstyles in the new hairstyle generation process. To verify the effectiveness of our model, CelebA-HQ (Large-scale CelebFaces Attribute) and FFHQ (Flickr-Faces-HQ) are adopted to train and test our proposed model. In the image evaluation test used, FID, PSNR, and SSIM image evaluation methods are used to test our model and compare it with other excellent image generation networks. Our proposed model is more robust in terms of test scores and real image generation.

The cortical and subcortical correlates of face pareidolia in the macaque brain.

Face detection is a foundational social skill for primates. This vital function is thought to be supported by specialized neural mechanisms; however, although several face-selective regions have been identified in both humans and nonhuman primates, there is no consensus about which region(s) are involved in face detection. Here, we used naturally occurring errors of face detection (i.e. objects with illusory facial features referred to as examples of 'face pareidolia') to identify regions of the macaque brain implicated in face detection. Using whole-brain functional magnetic resonance imaging to test awake rhesus macaques, we discovered that a subset of face-selective patches in the inferior temporal cortex, on the lower lateral edge of the superior temporal sulcus, and the amygdala respond more to objects with illusory facial features than matched non-face objects. Multivariate analyses of the data revealed differences in the representation of illusory faces across the functionally defined regions of interest. These differences suggest that the cortical and subcortical face-selective regions contribute uniquely to the detection of facial features. We conclude that face detection is supported by a multiplexed system in the primate brain.

Learning State Assessment in Online Education Based on Multiple Facial Features Detection.

Considering that most of online training is not effectively supervised, this article presents an online leaning state assessment approach which combines blink detection, yawn detection, and head pose estimation. Blink detection is realized by computing the eye aspect ratio and the ratio of closed eye frames to the total frames per unit time to evaluate the degree of eye fatigue. Yawn detection is implemented by computing the aspect ratio of the mouth by using the feature points of the inner lip and combining it with the time of opening mouth to distinguish the mouth state. Head pose estimation is first implemented by calculating the head rotation matrix by matching the feature points of 2D face with the 3D face model and then calculating the Euler angle of the head according to the rotation matrix to evaluate the change of the head pose. Especially in yawn detection, we employ the feature points of inner lips in the calculation of the mouth aspect ratio to avoid the impact of lip thickness of various participants. Furthermore, the blink detection, yawn detection, and head pose estimation are first calculated based on the two-dimensional grayscale image of human face, which could reduce the computational complexity and improve the real-time performance of detection. Finally, combining the values of blinking, yawning, and head pose, multiple groups of experiments are carried out to assess the state of different online learners; then, the learning state is evaluated by analyzing the numerical changes of the three characteristics. Experimental results show that our approach could effectively evaluate the state of online learning and provide support for the development of online education.

Automatic facial skin feature detection for everyone

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. S Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges. The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions. The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation. We employ Unet++ as the network architecture for feature detection. This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.

Heterogeneous face detection based on multi‐task cascaded convolutional neural network

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.

MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss

The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network; and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects.