Face Recognition Research Articles

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

Asymmetry of neural circuits for word and face recognition in readers of Roman and Arabic script

Asymmetry of neural circuits for word and face recognition in readers of Roman and Arabic script

Involuntary Eye-Movement Signatures Differ for Recognition of Oneself, Familiar and Unfamiliar Faces

Involuntary Eye-Movement Signatures Differ for Recognition of Oneself, Familiar and Unfamiliar Faces

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.

Comparing Human Face and Body Recognition at Various Distance and Rotation Viewing Conditions.

Comparing Human Face and Body Recognition at Various Distance and Rotation Viewing Conditions.

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

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

Neural correlates of familiar face recognition do not benefit from colour information

Neural correlates of familiar face recognition do not benefit from colour information

Exploring Spatial Frequency and Orientation Tunings for Face Recognition in Eight Cultural Groups

Exploring Spatial Frequency and Orientation Tunings for Face Recognition in Eight Cultural Groups

Investigating the impact of Gaussian noise on face recognition performance for humans and convolutional neural networks

Investigating the impact of Gaussian noise on face recognition performance for humans and convolutional neural networks

Fear of nothing to hide? How do Chinese people feel about privacy when facing facial recognition cameras?

ABSTRACT Facial-recognition cameras are becoming increasingly important for governments around the world to help maintain public security and improve public services. Despite these benefits, previous studies have shown that facial-recognition cameras may also cause risks such as privacy violations. This study explores how citizens respond to government-sponsored facial-recognition cameras in terms of privacy concerns in China, which is a country with a well-established digital system. By using a national survey combined with facial-recognition camera data, this study reveals that the presence of government-sponsored facial-recognition cameras does not lead to increased information privacy concerns among Chinese citizens. Instead, citizens in China are generally very willing to accept facial-recognition cameras built by governments, and this acceptance increases when citizens’ trust in companies and government institutions is greater. Our study suggests that the application of facial-recognition cameras has probably been normalised because people are more likely to consider the social and economic advantages of such application over the downside, specifically that related to privacy intrusion.

A Multi-Class Facial Emotion Recognition System for Still Images 1Omage Micheal, 2Fasola Olusanjo & 3Woods Nancy C. 1,3University of Ibadan, Ibadan, Nigeria 2Federal University of Technology, Minna, Nigeria E-mails: 1micheal.omage@gmail.com, 2Sanjo.fasola@futminna.edu.ng, 3Chyn.woods@gmail.com Phone Nos: 1+2348084121913, 2+2348023138034, 3+2348037273291 ABSTRACT Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of

Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of artificial intelligence and computer vision, recognizing these emotions from facial cues has gained significant importance. Most research focus on detecting a single dominant emotion on any given facial image, although an image could portray several emotions. This work presents a multi-class facial emotion recognition system capable of predicting and quantifying the presence of multiple emotions in a single facial image. The system employs a Convolutional Neural Network (CNN) architecture optimized for multi-label classification across eight emotion classes. The AffectNet dataset, comprising 27,836 images with diverse ages, ethnicities, and real-world settings, was utilized for training the model, to enhance generalization capabilities. The model's performance was evaluated on an impartial benchmark dataset, FER-Plus, containing 35,710 images. The CNN model performed well, with a micro F1-score of 0.7 and a macro F1-score of 0.7 on the test set from AffectNet, and a micro F1-score of 0.7 and a macro F1-score of 0.4 on the FER-Plus dataset. Qualitative analysis demonstrated the model's capability in recognizing blended and subtle facial expressions, providing significant probability distributions across multiple emotion labels. The developed system advances the state-of-the-art in multi-label facial emotion recognition by successfully modeling emotion mixtures, contributing to a deeper understanding of this challenging task and enabling practical applications in affective computing, human-computer interaction, psychological studies, and improving accessibility and inclusivity in emotional computing technologies. Keywords: Multi-Class, Facial Emotion Recognition, Security, System, Still Images, Models, Detection Omage, M. Fasola, O. & Woods, N.C. (2024): A Multi-Class Facial Emotion Recognition System for Still Images. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 67-80. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N3P6

Application of Convolutional Neural Network in Image Recognition

Image recognition is of great significance for both human social development and daily life. This paper mainly discusses the application research of convolutional neural network (CNN) in the field of image recognition. Firstly, the basic structure and working principle of CNN, including the core components of convolution layer, pooling layer and full connection layer, are introduced, and then the structure and characteristics of the middle layer are explained. Then, this paper compares the advantages and disadvantages of different CNN models such as LeNet-5, AlexNet, GoogLeNet, ResNet, etc., and analyzes their application in face recognition, medical image recognition, traffic recognition, character recognition and other fields. Finally, this paper summarizes the application of CNN in image recognition, and points out the problems faced in its practical application, such as model complexity, computational resource requirements, etc., aiming to provide reference for further research of image recognition technology.

Clinical efficacy of a head-mounted device for central vision loss

Central vision loss (CVL) is a major form of low vision that remains inadequately managed worldwide. This study evaluated the clinical efficacy of a novel head-mounted device (HMD), Onyx, designed to enhance visual function and vision-related quality of life for CVL patients. It employs a projection system that enables patients to leverage their residual peripheral vision for environmental awareness. It also integrates artificial intelligence to augment the automatic recognition of text, faces, and objects. In this single-center, prospective cohort study, 41 binocularly low vision patients with CVL were instructed to use Onyx for 4 to 6 h daily over a one-month period. Various metrics were assessed, including near and distance visual acuity (VA), recognition abilities for faces and objects, and the low vision quality-of-life (LVQOL) questionnaire scores, at the start and end of the study. The results showed significant improvements in near VA for 60.98% of the participants, distance VA for 80.49%, and recognition ability for 90.24%. 68.29% of the participants showed significant improvements in the LVQOL scores. Improvement in recognition ability was negatively correlated with baseline recognition ability. Additionally, improvement in the LVQOL scores was correlated with age and the baseline LVQOL score. Overall, the study found that the novel HMD significantly improved visual function and vision-related quality of life for low vision patients with CVL, highlighting the potential benefits and the need for further evaluation of such devices.

BFFN: A novel balanced feature fusion network for fair facial expression recognition

Facial expression recognition (FER) technology has become increasingly mature and applicable in recent years. However, it still suffers from the bias of expression class, which can lead to unfair decisions for certain expression classes in applications. This study aims to mitigate expression class bias through both pre-processing and in-processing approaches. First, we analyze the output of existing models and demonstrate the existence of obvious class bias, particularly for underrepresented expressions. Second, we develop a class-balanced dataset constructed through data generation, mitigating unfairness at the data level. Then, we propose the Balanced Feature Fusion Network (BFFN), a class fairness-enhancing network. The BFFN mitigates the class bias by adding facial action units (AU) to enrich expression-related features and allocating weights in the AU feature fusion process to improve the extraction ability of underrepresented expression features. Finally, extensive experiments on datasets (RAF-DB and AffectNet) provide evidence that our BFFN outperforms existing FER models, improving the fairness by at least 16%.

Моделирование чувствительности литийсодержащих кристаллических сцинтилляторов к нейтронному излучению

This paper presents the results of Monte Carlo modelling of the sensitivity of the lithium containing detectors CLYC, NaIL, CLLB to thermal neutrons. The difference between the sensitivity and the neutron freepath depth of the CLYC scintillator enriched and unenriched with the 6Li isotope is shown. The analysis of the distribution of alpha particles and neutrons in the scintillator volume is performed, showing the inefficiency of the unenriched scintillator due to the fact that not all neutrons react with the crystal material. Mathematical modelling by the Monte Carlo method of the scintillator sensitivity and its comparison with respect to the irradiation direction has been carried out. It is shown that for the scintillator in the face and side geometry with the size more than 38 mm the difference in sensitivity at change of the neutron flux direction is connected only with the scintillator cross section and 6Li concentration with the same value of the specific sensitivity. The obtained model results were compared with available experimental data for NaIL and CLLB scintillators, which showed the reliability of the developed models and calculation methods.

Which factors most strongly influence the popularity of facial recognition software on mobile device?

Which factors most strongly influence the popularity of facial recognition software on mobile device?

Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, including extensive experiments on the tasks of face identification and verification. Our study focuses on six state-of-the-art models: EfficientNet, Inception, MobileNet, ResNet, VGG, and Vision Transformers. Our evaluation of these models is based on five diverse datasets: Labeled Faces in the Wild, Real World Occluded Faces, Surveillance Cameras Face, UPM-GTI-Face, and VGG Face 2. These datasets present unique challenges regarding people diversity, distance from the camera, and face occlusions such as those produced by masks and glasses. Our contribution to the field includes a deep analysis of the experimental results, including a thorough examination of the training and evaluation process, as well as the software and hardware configurations used. Our results show that Vision Transformers outperform Convolutional Neural Networks in terms of accuracy and robustness against distance and occlusions for face recognition related tasks, while also presenting a smaller memory footprint and an impressive inference speed, rivaling even the fastest Convolutional Neural Networks. In conclusion, our study provides valuable insights into the performance of Vision Transformers for face recognition related tasks and highlights the potential of these models as a more efficient solution than Convolutional Neural Networks.

AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis

AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis

Study on the Generation and Comparative Analysis of Ethnically Diverse Faces for Developing a Multiracial Face Recognition Model

Study on the Generation and Comparative Analysis of Ethnically Diverse Faces for Developing a Multiracial Face Recognition Model

B - 84 Memory and Attention Predict Narcissistic Personality Characteristics

Abstract Objective Narcissistic Personality Disorder (NPD) entails grandiosity, lack of empathy, and a need for admiration. Research suggests a distinct cognitive profile linked to narcissistic characteristics, including facial recognition and memory issues. Our study examined memory and attention predictors of NPD characteristics in a non-clinical sample, filling a research gap. Method Young adult (M = 19.9, SD = 3.4) university students (N = 109) were participants. They identified primarily as women (52.3%) or men (47.7%) and White (65.1%) or African American (29.4%). Measures included Test of Everyday Attention (Robertson et al., 1996) and the Wechsler Memory Scale – IV (Wechsler, 2009) to assess memory and attention functioning. The Millon Clinical Multiaxial Inventory – III (Millon et al., 2006) was used to measure narcissistic personality disorder characteristics. The Test of Nonverbal Intelligence-4 (Brown et al., 2010) was used to estimate IQ. Results Regression analysis revealed significant associations between memory/attention facets and NPD characteristics, F(7, 101) = 2.18, p = 0.04, adj. R2 = 0.07, 95% CI [0, 0.12]. Divided attention (β = −0.236, p = 0.02) and associative memory (β = −0.222, p = 0.04) were significant predictors. After controlling for IQ, associative memory remained significant (β = −0.257, p = 0.02), while divided attention did not (β = −0.102, p = 0.32). Verbal list learning emerged as a risk factor (β = 0.258, p = 0.04). Conclusions Our findings align with prior research, indicating cognitive correlates of NPD, particularly poorer associative memory and better verbal list learning. These results suggest a unique cognitive profile for NPD, characterized by high verbal list learning and poor associative memory. Understanding these cognitive underpinnings sheds light on the complexity of NPD and may inform interventions.