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Journal articles on the topic 'Facial video processing'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Kroczek, Leon O. H., Angelika Lingnau, Valentin Schwind, Christian Wolff, and Andreas Mühlberger. "Angry facial expressions bias towards aversive actions." PLOS ONE 16, no. 9 (2021): e0256912. http://dx.doi.org/10.1371/journal.pone.0256912.

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Social interaction requires fast and efficient processing of another person’s intentions. In face-to-face interactions, aversive or appetitive actions typically co-occur with emotional expressions, allowing an observer to anticipate action intentions. In the present study, we investigated the influence of facial emotions on the processing of action intentions. Thirty-two participants were presented with video clips showing virtual agents displaying a facial emotion (angry vs. happy) while performing an action (punch vs. fist-bump) directed towards the observer. During each trial, video clips s

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Muthanna Shibel, Ahmed, Sharifah Mumtazah Syed Ahmad, Luqman Hakim Musa, and Mohammed Nawfal Yahya. "DEEP LEARNING DETECTION OF FACIAL BIOMETRIC PRESENTATION ATTACK." LIFE: International Journal of Health and Life-Sciences 8 (October 23, 2023): 61–78. http://dx.doi.org/10.20319/lijhls.2022.8.6178.

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Face recognition systems have gained increasing importance in today’s society, which applications range from access controls to secure systems to electronic devices such as mobile phones and laptops. However, the security of face recognition systems is currently being threatened by the emergence of spoofing attacks that happens when someone tries to unauthorizedly bypass the biometric system by presenting a photo, 3-dimensional mask, or replay video of a legit user. The video attacks are perhaps one of the most frequent, cheapest, and simplest spoofing techniques to cheat face recognition syst

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Mahalim, Vaishnavi Sanjay, Seema Goroba Admane, Divya Vinod Kundgar, and Ankit Hirday Narayan Singh. "Development of Real-Time Emotion Recognition System Using Facial Expressions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26415.

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This research presents a real-time emotion recognition system that combines human-friendly machine interaction with picture processing. For many years, facial detection has been available. Moving further, it is possible to simulate the emotions that people express on their faces and experience in their brains through the use of video, electric signals, or image forms. Since it is hard for computers to detect emotions from images or videos and a difficult task for the human eye, machine emotion detection requires a variety of image processing approaches for feature extraction. The approach prop

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Tej, Maddimsetty Bullaiaha. "Eye of Devil: Face Recognition in Real World Surveillance Video with Feature Extraction and Pattern Matching." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2334–37. http://dx.doi.org/10.22214/ijraset.2021.39711.

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Abstract: People lost, people missing etc., these are the words we come across whenever there is any mass gathering events going on or in crowded areas. To solve this issue some traditional approaches like announcements are in use. One idea is to identify the person using face recognition and pattern matching techniques. There are several techniques to implement face recognition like extraction of facial features by using the position of eyes, nose, jawbone or skin texture analysis etc., By using these techniques a unique dataset can be created for each human. Here the photograph of the missin

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Jasna A, Rathi, and Dr T. C. . Subbulakshmi. "Law Enforcement Facial Recognition System for Crime." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43842.

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Facial recognition technology is used by police enforcement to identify offenders and find missing people. This paper describes a law enforcement facial recognition system that detects and recognizes faces using the Local Binary Pattern Histogram (LBPH) and the Haar Cascade Classifier. The system's simple Tkinter-based graphical user interface (GUI) enables authorized workers to register photographs of offenders and missing people, analyse images, and process films for facial recognition. To offer best data handling and storage, the system is built with Python (Tkinter), PHP, MySQL (phpMyAdmin

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Blanes-Vidal, Victoria, Tomas Majtner, Luis David Avendaño-Valencia, Knud B. Yderstraede, and Esmaeil S. Nadimi. "Invisible Color Variations of Facial Erythema: A Novel Early Marker for Diabetic Complications?" Journal of Diabetes Research 2019 (September 2, 2019): 1–7. http://dx.doi.org/10.1155/2019/4583895.

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Aim. (1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this “unveiled” facial red coloration and its periodic variations present specific characteristics in diabetic patients different from those in control subjects. Methods. We video recorded the faces of 20 diabetic patients with peripheral neuropathy, retinopathy, and/or nephropathy and 10 nondiabetic co

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S, Manjunath, Banashree P, Shreya M, Sneha Manjunath Hegde, and Nischal H P. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 129–35. http://dx.doi.org/10.22214/ijraset.2022.42109.

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Abstract: Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the

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Singh,, Mr Ankit. "Real-Time Emotion Recognition System Using Facial Expressions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31021.

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This paper describes an emotion detection system based on real-time detection using image processing with human-friendly machine interaction. Facial detection has been around for decades. Taking a step ahead, Human expressions displayed by face and felt by the brain, captured via video, electric signal, or image form can be approximated. To recognize emotions via images or videos is a difficult task for the human eye and challenging for machines thus detection of emotion by a machine requires many image processing techniques for feature extraction. This paper proposes a system that has two mai

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Lee, Seongmin, Hyunse Yoon, Sohyun Park, Sanghoon Lee, and Jiwoo Kang. "Stabilized Temporal 3D Face Alignment Using Landmark Displacement Learning." Electronics 12, no. 17 (2023): 3735. http://dx.doi.org/10.3390/electronics12173735.

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One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome the problem, we introduce neural networks to reconstruct stable and precise faces in time. The reconstruction network extracts the 3DMM parameters from video sequences to represent 3D faces in time. Meanwhile, our displacement networks learn the changes in facial landmarks. In particular, the networks learn changes caused by facial identit

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Rocha Neto, Aluizio, Thiago P. Silva, Thais Batista, Flávia C. Delicato, Paulo F. Pires, and Frederico Lopes. "Leveraging Edge Intelligence for Video Analytics in Smart City Applications." Information 12, no. 1 (2020): 14. http://dx.doi.org/10.3390/info12010014.

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In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system

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Uzaifuddin Ahmeduddin,, Mohammed. "Facial Recognition-Based Home Security System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem03956.

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ABSTRACT: Face detection has appeared as a vital component of modern security systems, with applications ranging from mobile authentication to surveillance. This research explores a face detection system designed using image processing techniques to enhance home security. The system captures live video, detects faces, and classifies them as either family members or unknown individuals. On identifying an unknown face, the system sends a mobile notification to the homeowner. This paper presents the methodology, implementation, and performance evaluation of the system, highlighting its potential

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Mr., K. Shyam Babu, and Sri Ram K. Srirag Chowdary G. Nagaraj Ch. Bhanu Chandu K. "FACIAL EMOTION RECOGNITION & DETECTION IN PYTHON USING DEEP LEARNING." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 277–79. https://doi.org/10.5281/zenodo.15459308.

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The paper introduces a real-time facial emotion detection system based on Convolutional Neural Networks(CNNs) and OpenCV. The system detects the faces and identifies emotions from facial expressions byprocessing video frames in real time. The model of CNN is trained on a big facial image dataset and emotions,and the performance shows accurate and speedy emotion detection. Integration of OpenCV with CNN modelfacilitates real-time processing of video frames, and thus the system is applicable in practical purposes.Application of machine learning includes the recognition of facial emotions of emot

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Gosavi, Prof Amol. "Deepfake Video Face Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5840–47. https://doi.org/10.22214/ijraset.2025.69233.

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The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This rese

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Selva, Selva, and Selva Kumar S. "Hybridization of Deep Sequential Network for Emotion Recognition Using Unconstraint Video Analysis." Journal of Cybersecurity and Information Management 13, no. 2 (2024): 109–23. http://dx.doi.org/10.54216/jcim.130209.

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The reliable way to discern human emotions in various circumstances has been proven to be through facial expressions. Facial expression recognition (FER) has emerged as a research topic to identify various essential emotions in the present exponential rise in research for emotion detection. Happiness is one of these basic emotions everyone may experience, and facial expressions are better at detecting it than other emotion-measuring methods. Most techniques have been designed to recognize various emotions to achieve the highest level of general precision. Maximizing the recognition accuracy fo

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Namboodiri, Sandhya Parameswaran, and Venkataraman D. "A computer vision based image processing system for depression detection among students for counseling." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 503. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp503-512.

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Psychological problems in college students like depression, pessimism, eccentricity, anxiety etc. are caused principally due to the neglect of continuous monitoring of students’ psychological well-being. Identification of depression at college level is desirable so that it can be controlled by giving better counseling at the starting stage itself. The disturbed mental state of a student suffering from depression would be clearly evident in the student’s facial expressions.Identification of depression in large group of college students becomes a tedious task for an individual. But advances in t

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Tanwar, Arnav. "Real-Time Video Surveillance Face Detection System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46329.

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Abstract——The Real-Time Video Surveillance Face Detection System offers a modernized solution for automated surveillance by leveraging Python, Django, and OpenCV to achieve reliable facial recognition in security-sensitive environments. Utilizing cascaded classifiers, as proposed by Jones and Viola [1], this system rapidly identifies faces in real-time, while managing a dynamic profile database. Additionally, Saraswat and Kushwaha's work on CCTV-based face detection [2] informs our approach to handling challenges like low lighting and crowd density. Our methodology combines efficient video pro

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Li, Yingjiu, Yan Li, and Zilong Wang. "FaceCloseup: Enhancing Mobile Facial Authentication with Perspective Distortion-Based Liveness Detection." Computers 14, no. 7 (2025): 254. https://doi.org/10.3390/computers14070254.

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Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims’ faces to bypass authentication mechanisms. The increasing availability of personal images on socia

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Bhat, Mr Abhilash L., N. Nithesh Kumar, Poojitha Y, Siripireddy Thulasi, and V. Arvind. "CNN Based Facial Recognition with Age Invariance." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1061–65. http://dx.doi.org/10.22214/ijraset.2023.56680.

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Abstract : As the world has seen exponential expansion over the previous decade, there has been an unusual increase in the crime rate as well as an increase in the number of criminals/missing persons. Face recognition can extract the individualistic characteristics of the human face. A straightforward and adaptable biometric technology is face recognition. The technology used to recognize and identify faces in images or videos is called face detection and recognition. The process of removing facial features has gotten easier as technology has advanced. This study describes the use of an automa

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Xavier Lemos, Dayllon Vinícius, Humberto Jose Longo, Márcia Rodrigues Cappelle Santana, Mariana Dourado Ximenes de Sena Santos, Sanderson Oliveira de Macedo, and Ronaldo Martins da Costa. "YOLO and CNN for Cat Detection and Recognition." Revista de Informática Teórica e Aplicada 32, no. 1 (2025): 158–65. https://doi.org/10.22456/2175-2745.143684.

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This paper proposes a processing architecture for recognizing domestic felines in videos and images based on facial features. Some photos of the cats to be identified in the videos and images need to be collected in advance. A key aspect of this study is avoiding the need to retrain the model whenever the set of cats to be identified changes. The architecture involves five processing stages: video processing, YOLOv8 for cat detection, InceptionV3 for feature extraction, KNN for database searching, and voting process to improve classification. Transfer learning and fine-tuning techniques are em

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P.Dahake, R., and M. U. Kharat. "Face Detection and Processing: a Survey." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1066. http://dx.doi.org/10.14419/ijet.v7i4.19.28287.

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In the recent era facial image processing is gaining more importance and the face detection from image or from video have number of applications which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some c

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Syahputra, Eswin, Irpan Nursukmi, Sony Putra, Bayu Sukma Sani, and Rian Farta Wijaya. "EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET." ZERO: Jurnal Sains, Matematika dan Terapan 6, no. 2 (2023): 147. http://dx.doi.org/10.30829/zero.v6i2.14751.

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<span lang="id">Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing. However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extrac

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Abraham, Jobin Reji, Jobin, Saniya P. M, George Dominic, and M. Arjun. "Surveillance System with Face Recognition Using Hog." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 199–206. http://dx.doi.org/10.22214/ijraset.2022.47854.

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Abstract: Due to various suspicious activity, the surveillance system continues to emerge in the technical field. Everyday security threats can have a serious impact on people's day-today activity. In this area, numerous techniques have been developed, however some issues have not yet been overcome. The research described in this white paper offers more precise video surveillance with less processing complexity. The system's most crucial component includes location, recognition and facial recognition. The technology pulls highlighted facial information from a live environment or video dataset.

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Arulselvan, Mr M. "Deep Learning-Based Facial Attendance Tracker." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3495–500. https://doi.org/10.22214/ijraset.2025.69036.

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To develop a real-time face attendance recognition system that utilizes facial recognition technology, integrated with advanced anti-spoofing techniques. This approach ensures a secure, contactless method for marking attendance, significantly reducing the risk of unauthorized or fraudulent entries. The system aims to identify and prevent spoofing attempts, such as the use of photos, pre-recorded videos, or masks, during the authentication process.The core functionality involves processing realtime video input, detecting faces frame-by-frame, and accurately matching them with a pre-registered d

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Faqeda, Faqeda, and Mohammed Abdullah Naser. "Enhanced Face Detection in Videos Based on Integrating Spatial Features (LBP, CS-LBP) with CNN Technique." Journal of Cybersecurity and Information Management 14, no. 2 (2024): 343–51. http://dx.doi.org/10.54216/jcim.140225.

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Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection.

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Savostyanov, A. N., E. G. Vergunov, A. E. Saprygin, and D. A. Lebedkin. "Validation of a face image assessment technology to study the dynamics of human functional states in the EEG resting-state paradigm." Vavilov Journal of Genetics and Breeding 26, no. 8 (2023): 765–72. http://dx.doi.org/10.18699/vjgb-22-92.

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The article presents the results of a study aimed at finding covariates to account for the activity of implicit cognitive processes in conditions of functional rest of the subjects and during them being presented their own or someone else’s face in a joint analysis of EEG experiment data. The proposed approach is based on the analysis of the dynamics of the facial muscles of the subject recorded on video. The pilot study involved 18 healthy volunteers. In the experiment, the subjects were sitting in front of a computer screen and performed the following task: sequentially closed their eyes (th

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Zheng, Yufeng. "Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos." BioMedInformatics 5, no. 1 (2025): 6. https://doi.org/10.3390/biomedinformatics5010006.

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Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLST

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S, Suriya, Anubharathi V S, Bharathi G, Harshada R, Vignesh Aditya R, and Sundarsree B G. "Oculang: Empowering Communication through Blink Language Detection." IRO Journal on Sustainable Wireless Systems 6, no. 4 (2025): 318–32. https://doi.org/10.36548/jsws.2024.4.003.

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Oculang is a communication system that uses computer vision and machine learning techniques to enable individuals with neurodegenerative disorders to communicate using eye gestures. The application focuses on detecting facial landmarks, gaze estimation, and blink detection to analyze combinations of eye movements captured from video input and produce message outputs. Dlib’s shape predictor (68 facial landmarks) and OpenCV-based image processing methods are used to extract and process the features of the eye region. A decision-making algorithm maps the detected gestures and predefined keywords

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Bianchini, Edoardo, Domiziana Rinaldi, Marika Alborghetti, et al. "The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson’s Disease." Brain Sciences 14, no. 1 (2024): 109. http://dx.doi.org/10.3390/brainsci14010109.

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Facial movements are crucial for social and emotional interaction and well-being. Reduced facial expressions (i.e., hypomimia) is a common feature in patients with Parkinson’s disease (PD) and previous studies linked this manifestation to both motor symptoms of the disease and altered emotion recognition and processing. Nevertheless, research on facial motor impairment in PD has been rather scarce and only a limited number of clinical evaluation tools are available, often suffering from poor validation processes and high inter- and intra-rater variability. In recent years, the availability of

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Sridhar, M. Bhanu, Sai Himaja Kinthada, and Bhargavi Marni. "A Unique Framework for Contactless Estimation of Body Vital Signs using Facial Recognition." International Journal of Computer Science and Mobile Computing 10, no. 12 (2021): 14–20. http://dx.doi.org/10.47760/ijcsmc.2021.v10i12.002.

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As one of the consequences of COVID-19 pandemic, a lot of new technologies are developing in fast-track pace in clinical practices. The main idea of our project is to design contactless technology for the support of patients who suffer from blood pressure disorders and coronary heart diseases using machine learning approach. This may intend people to monitor their heart rate, pulse rate, respiratory life and oxygen saturation levels at an ease. The orientation of this paper is to monitor the blood pressure considering the facial changes and movements in a video to get rid of cuff-based measure

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Gillioz, Christelle, Maroussia Nicolet-dit-Félix, Sylvain Delplanque, Marcello Mortillaro, David Sander, and Marina Fiori. "How do emotionally intelligent individuals react to other people's emotions? A study on emotional and facial reactions." BMC Psychol 13 (May 10, 2025): Nr. 224. https://doi.org/10.1186/s40359-025-02425-5.

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Background According to the hypersensitivity hypothesis, highly emotionally intelligent individuals perceive emotion information at a lower threshold, pay more attention to emotion information, and may be characterized by more intense emotional experiences. The goal of the present study was to investigate whether and how emotional intelligence (EI) is related to hypersensitivity operationalized as heightened emotional and facial reactions when observing others narrating positive and negative life experiences. Methods Participants (144 women) watched positive and negative videos in three d

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Poorna, S. S., S. Devika Nair, Arsha Narayan, et al. "Bimodal Emotion Recognition Using Audio and Facial Features." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 189–94. http://dx.doi.org/10.1166/jctn.2020.8649.

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A multimodal emotion recognition system is proposed using speech and facial images. For this purpose a video database is developed, containing emotions in three affective states viz. anger, sad and happiness. The audio and the snapshots of facial expressions acquired from the videos constituted the bimodal input for recognizing emotions. The spoken sentences in the database included text dependent as well as text independent sentences in Malayalam language. The audio features included short-time processing of speech to obtain: energy, zero crossing count, pitch and Mel Frequency Cepstral Coeff

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Norwahidah, Ibrahim, Razali Md Tomari Mohd, and Nurshazwani Wan Zakaria Wan. "Analysis of minimum face video duration and the effect of video compression to image-based non-contact heart rate monitoring system." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 403–10. https://doi.org/10.11591/eei.v9i1.1855.

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Heart rate (HR) is one of important indicator for human physiological diagnosis, and camera can be used to detect it via photoplethysmograph (PPG) signal extraction. In doing so, number of sample images required to measure the HR signal, and quality of the images itself are important to yield an accurate reading. This paper tackles such an issue by analyzing the effect of sampling interval to HR reading in compressed and original video format, obtained in various ranging locations. Technically, important facial points from video stream were estimated by using cascade regression facial tracker.

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Komar, Mayur, Vinod Kolhe, Preeti Gajul, and Rohan Bhukan. "Video Based Student Attendance Management System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1788–93. http://dx.doi.org/10.22214/ijraset.2022.42628.

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Abstract: Mainly there are two conventional methods of marking attendance which are calling out the roll call or by taking student sign on paper. They both were more time consuming and difficult. Hence, there is a requirement of computer-based student attendance management system which will assist the faculty for maintaining attendance record automatically. In this project we have implemented the automated attendance system using MATLAB. We have projected our ideas to implement “Automated Attendance System Based on Facial Recognition”, in which it imbibes large applications. Keywords: Attendan

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Antoszczyszyn, P. M., J. M. Hannah, and P. M. Grant. "Facial Motion Analysis for Content-based Video Coding." Real-Time Imaging 6, no. 1 (2000): 3–16. http://dx.doi.org/10.1006/rtim.1998.0152.

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Mamatov, N., N. Niyozmatova, K. Erejepov, I. Narzullayev, Sh. Tojiboyeva, and A. Najmiddinov. "ALGORITHMS FOR IMPROVING THE QUALITY OF FACIAL IMAGES." Sciences of Europe, no. 138 (April 10, 2024): 64–72. https://doi.org/10.5281/zenodo.10957348.

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Image quality has a great impact on the accuracy of system performance in face recognition based on facial image analysis. Therefore, it is necessary to properly organize the process of taking pictures and pre-processing them using a photo or video camera. Pre-processing of images allows for improved image quality. Therefore, this research work is dedicated to the research of face image quality enhancement algorithms, which have been studied mainly divided into two large groups known as spatial and frequency methods. Also, the main problems that arise in the development of personal identificat

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Peng, Zhuang, Boyi Jiang, Haofei Xu, Wanquan Feng, and Juyong Zhang. "Facial optical flow estimation via neural non-rigid registration." Computational Visual Media 9, no. 1 (2022): 109–22. http://dx.doi.org/10.1007/s41095-021-0267-z.

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AbstractOptical flow estimation in human facial video, which provides 2D correspondences between adjacent frames, is a fundamental pre-processing step for many applications, like facial expression capture and recognition. However, it is quite challenging as human facial images contain large areas of similar textures, rich expressions, and large rotations. These characteristics also result in the scarcity of large, annotated real-world datasets. We propose a robust and accurate method to learn facial optical flow in a self-supervised manner. Specifically, we utilize various shape priors, includ

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M B, Ranjan. "Detection of Face Swapped Deep Fake Videos." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47643.

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ABSTRACT The Deepfake Detector is a novel application designed to enhance digital media integrity by identifying manipulated videos. This project centers on the creation and deployment of an advanced system that continuously analyzes uploaded video content to detect deepfakes. The system employs a deep learning model trained on real and fake video data, utilizing facial recognition and temporal analysis techniques. If a video is determined to be manipulated, the system informs the user with a confidence score and visual indicators, mitigating potential risks associated with deceptive media. Th

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Hajarolasvadi, Noushin, Enver Bashirov, and Hasan Demirel. "Video-based person-dependent and person-independent facial emotion recognition." Signal, Image and Video Processing 15, no. 5 (2021): 1049–56. http://dx.doi.org/10.1007/s11760-020-01830-0.

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Antoszczyszyn, P. M., J. M. Hannah, and P. M. Grant. "Reliable tracking of facial features in semantic-based video coding." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 4 (1998): 257. http://dx.doi.org/10.1049/ip-vis:19982153.

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. "Recognition of emotions from video using acoustic and facial features." Signal, Image and Video Processing 9, no. 5 (2013): 1029–45. http://dx.doi.org/10.1007/s11760-013-0522-6.

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Hajarolasvadi, Noushin, and Hasan Demirel. "Deep facial emotion recognition in video using eigenframes." IET Image Processing 14, no. 14 (2020): 3536–46. http://dx.doi.org/10.1049/iet-ipr.2019.1566.

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Oguine, Ozioma Collins, Kanyifeechukwu Jane Oguine, Hashim Ibrahim Bisallah, and Daniel Ofuani. "Hybrid facial expression recognition (FER2013) modelfor real-time emotion classification and prediction." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 1, no. 1 (2022): 56–64. http://dx.doi.org/10.54646/bijiam.2022.09.

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Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gamingto human-computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expressionrecognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learningarchitecture. The objective is to classify real-time and digital facial images into one of the seven facial emotioncategories considered. The DCNN employed in this research has more convolutional layers, ReLU activationfunctions, and multiple kernels to enhance filterin

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D’Ulizia, Arianna, Alessia D’Andrea, Patrizia Grifoni, and Fernando Ferri. "Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review." Applied Sciences 13, no. 16 (2023): 9188. http://dx.doi.org/10.3390/app13169188.

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Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and classified according to the main steps of the facial-deception-detection process

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Wang, Rui Hu, and Bin Fang. "Emotion Fusion Recognition for Intelligent Surveillance with PSO-CSVM." Advanced Materials Research 225-226 (April 2011): 51–56. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.51.

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The next generation of intelligent surveillance system should be able to recognize human’s spontaneous emotion state automatically. Compared to speaker recognition, sensor signals analyzing, fingerprint or iris recognition, etc, facial expression and body gesture processing are two mainly non-intrusive vision modalities, which provides potential action information for video surveillance. In our work, we care one kind of facial expression, i.e. anxiety and gesture motion only. Firstly facial expression and body gesture feature are extracted. Particle Swarm Optimization algorithm is used to sele

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Sumathi, J. k. "Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework." Wasit Journal of Computer and Mathematics Science 1, no. 1 (2021): 1–8. http://dx.doi.org/10.31185/wjcm.vol1.iss1.3.

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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platfo

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Zekhnine, Chérifa, and Nasr Eddine Berrached. "Human-Robots Interaction by Facial Expression Recognition." International Journal of Engineering Research in Africa 46 (January 2020): 76–87. http://dx.doi.org/10.4028/www.scientific.net/jera.46.76.

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This paper presents a facial expressions recognition system to command both mobile and arm robot. The proposed system mainly consists of two modules: facial expressions recognition and robots command. The first module aims to extract the ROI (Region Of Interest like: mouth, eyes, eyebrow) using Gradient Vector Flow (GVF) snake segmentation and the Euclidian distance calculation (compatible with the MPEG-4 description of the six universal emotions). To preserve the temporal aspect of the processing from FEEDTUM database (video file), Time Delay Neural Network (TDNN) is used as classifier of the

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Chu, Wangbin, and Yepeng Guan. "Identity Verification Based on Facial Pose Pool and Bag of Words Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 3 (2017): 448–55. http://dx.doi.org/10.20965/jaciii.2017.p0448.

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There are many challenges for face based identity verification. It is one of fundamental topics in image processing and video analysis, and so on. A novel approach has been developed for facial identity verification based on a facial pose pool, which is constructed in an incremental clustering way to find both facial spatial information and orientation diversity. Bag of words is selected to extract image features from the facial pose pool in affine SIFT descriptor. The visual codebook is generated ink-means and Gaussian mixture model. Posterior pseudo probabilities are used to compute the simi

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Liao, Yuxuan, Zhenyu Tang, Jiehong Lei, Jiajia Chen, and Zhong Tang. "Video Face Detection Technology and Its Application in Health Information Management System." Scientific Programming 2022 (February 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/3828478.

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Computer face detection, as an early step and prerequisite for applications such as face recognition and face analysis, has attracted people's attention for a long time. With the popularization of computer applications, the improvement of performance, and the gradual maturity of research in the field of image processing and pattern recognition, face-related applications have become more and more a reality, so the research on face detection and positioning is also receiving more and more attention. Face detection and positioning are an important part of face analysis technology. Its goal is to

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Jakkaew, Prasara, and Takao Onoye. "Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging." Sensors 20, no. 21 (2020): 6307. http://dx.doi.org/10.3390/s20216307.

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Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark v

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Dewi, Christine, Rung-Ching Chen, Xiaoyi Jiang, and Hui Yu. "Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks." PeerJ Computer Science 8 (April 18, 2022): e943. http://dx.doi.org/10.7717/peerj-cs.943.

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Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations

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