Face Recognition Research Articles

Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

Evaluating the benefits and risks of facial recognition payment systems adoption in Malaysia

PurposeCurrent developments in the FinTech payment industry have shown a rapid revolution in Industry 4.0, and understanding the factors affecting individual acceptance of facial recognition payment (FRP) is crucial. Hence, this study aims to evaluate the benefits and risks of FRP system adoption in Malaysia.Design/methodology/approachThe perceived risks and benefits framework is adopted as the foundation in this study to examine the various risks and benefits that users perceive, along with the trust factor, to study the relationships between these variables. Data were collected via an online questionnaire, and the hypotheses were tested using Partial Least Squares analysis on 277 responses.FindingsThe results revealed that perceived risk is a significant predictor of users' intention to use the FRP system. Privacy risk and financial risk significantly influence perceived risks, while security risk does not. Although convenience, perceived ease of use and perceived trust positively influence perceived benefits, perceived benefits do not significantly influence adoption intention. Moreover, perceived trust negatively affects perceived risks while positively affecting both perceived benefits and adoption intention. Additionally, personal innovativeness moderates the relationship between perceived risks and the intention to use the FRP system.Practical implicationsThis study helps policymakers and service providers understand individuals’ concerns and expectations regarding FRP systems. It aids practitioners in developing strategies to build trust, address innovativeness differences and mitigate risks, serving as a roadmap for integrating these systems into Malaysia's financial landscape.Originality/valueThis study distinguishes itself from prior research by evaluating FRP system adoption in Malaysia through the lens of perceived risks and benefits framework. It also explores personal innovativeness as a moderator, examining its impact on the relationship between usage intention and perceived risks and benefits. Additionally, it highlights perceived trust as a crucial factor influencing individuals' intention to adopt FRPs.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

Automated face recognition assists with low-prevalence face identity mismatches but can bias users.

We present three experiments to study the effects of giving information about the decision of an automated face recognition (AFR) system to participants attempting to decide whether two face images show the same person. We make three contributions designed to make our results applicable to real-word use: participants are given the true response of a highly accurate AFR system; the face set reflects the mixed ethnicity of the city of London from where participants are drawn; and there are only 10% of mismatches. Participants were equally accurate when given the similarity score of the AFR system or just the binary decision but shifted their bias towards match and were over-confident on difficult pairs when given only binary information. No participants achieved the 100% accuracy of the AFR system, and they had only weak insight about their own performance.

Ultrathin, Stretchable, and Twistable Ferroelectret Nanogenerator for Facial Muscle Detection

Ferroelectret nanogenerators (FENGs) have garnered attention due to their unique porous structure and excellent piezoelectric performance. However, most existing FENGs lack sufficient stretchability and flexibility, limiting their application in the field of wearable electronics. In this regard, we have focused on the development of an ultrathin, stretchable, and twistable ferroelectret nanogenerator (UST-FENG) based on Ecoflex, which is made up of graphene, Ecoflex, and anhydrous ethanol, with controllable pore shape and density. The UST-FENG has a thickness of only 860 µm, a fracture elongation rate of up to 574%, and a Young’s modulus of only 0.2 MPa, exhibiting outstanding thinness and excellent stretchability. Its quasi-static piezoelectric coefficient is approximately 38 pC/N. Utilizing this UST-FENG device can enable the recognition of facial muscle movements such as blinking and speaking, thereby helping to monitor people’s facial conditions and improve their quality of life. The successful application of the UST-FENG in facial muscle recognition represents an important step forward in the field of wearable systems for the human face.

Early Impairment of Face Perception in Post-Stroke Depression: An ERP Study.

Objective: Face recognition is an important cognitive function of the human brain. Post stroke depression (PSD) is a common mental complication after stroke, which has a serious impact on individual physical function recovery and quality of life. This study aims to explore the face perception characteristics of PSD through electrophysiological indicators N170 and VPP, and provide an objective basis for the early evaluation of facial cognitive dysfunction in PSD. Methods: 58 patients in the cerebral small vessel disease (CSVD) with depressive symptoms (PSD) and 188 patients in the pure CSVD (NPSD). At the same time, 30 healthy subjects were selected as the healthy controls (HC). The differences of N170 and VPP components between the three groups were compared under the stimulation of inverted faces and upright faces. Results: PSD patients exhibited significantly longer peak latency and lower amplitude of N170 and VPP under both inverted and upright face stimulation compared to HC and NPSD. These results suggest that PSD patients have defects in early face recognition, there are abnormalities in the early perception and structural encoding of face information, and both the "overall mechanism" and "feature mechanism" of face recognition are damaged. Conclusions: These findings provide neuroelectrophysiological evidence for impaired emotionless face recognition in PSD patients.

Group information enhances recognition of both learned and unlearned face appearances.

Are people better at recognizing individuals of more relevant groups, such as ingroup compared to outgroup members or high-status compared to low-status individuals? Previous studies that associated faces with group information found a robust effect of group on face recognition but only tested it using the same images presented during the learning phase. They therefore cannot tell whether group information enhances encoding of the specific image presented during learning or encoding of the person who appears in it, which should generalize to other images of that person. In addition, the measures used in these studies do not sufficiently distinguish between sensitivity and response bias. In this article, we addressed these limitations and examined in three experiments the effect of group membership (Experiments 1 and 2) and social status (Experiment 3) on face recognition. In all experiments, we assessed recognition of both learned and unlearned views of the learned faces. Our results show improved recognition of ingroup members compared to outgroup members and of individuals of high-status groups compared to low-status groups for both learned and unlearned views. These effects emerged also when we used measures of memory accuracy that adequately control for response bias. These findings highlight the importance of group and status information in person recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Age differences in mental state inference of sarcasm: Contributions of facial emotion recognition and cognitive performance.

Older age is associated with poorer ability to accurately infer mental states, but some mental states are more complex than others. Sarcasm is a complex mental state because the literal and intended meaning of a speaker's words are in opposition. Individuals must rely on additional cues (e.g., facial expressions, intonation) for accurate inference. We hypothesized that understanding of sarcastic versus sincere exchanges would be more sensitive to age-related difficulty in mental state understanding. We examined accuracy at identifying sarcasm among 263 adults (ages 18-90 years) using videos of social interactions in The Awareness of Social Inference Test. Hypotheses were tested using a logistic linear mixed effects model predicting correct/incorrect trial-level responses. To characterize why sarcasm differed with age, we measured two abilities commonly implicated in mental state understanding: facial emotion recognition and cognitive performance. Sarcasm understanding declined with age, whereas understanding of sincere exchanges did not. Both better emotion recognition and cognitive performance related to better understanding of sarcastic but not sincere exchanges. Only cognitive performance showed an age-related effect such that the cognitive performance among the oldest participants facilitated their understanding of both sarcastic and sincere exchanges. We showed that individual variation related to age and social and cognitive performance is more pronounced when the use of multiple mental state cues is more (sarcasm) or less (sincerity) necessary for accurate understanding of social interactions. Naturalistic paradigms involving multiple mental state cues can address important questions about how older adults make decisions in the real world.

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression

ObjectiveDepression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis.MethodsWe recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals.ResultsCompared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition.ConclusionThis study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

Implementing Facial Recognition Technology in a University Setting: An Organizational Justice Perspective

Facial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives.

Facial Image expression recognition and prediction system.

Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural models to produce a facial expression prediction system that looks like this: The first model is on using a support vector machine to carry out a classification task. As a follow-up to the second model, an attempt was made to create a Convolution Neural Network (CNN) using the VGG-NET (Visual Geometry Group Network). Following analysis of the results, an attempt was made to enhance the outcome using the third model, which used convolutional sequential layers linked to seven distinct expressions, and an inference was drawn based on loss and accuracy metric behavior. We will use a dataset of human picture facial images in this research, which has more than 35500 facial photographs and represents seven different types of facial expressions. We will analyze our data and make every effort to remove as much noise as we can before feeding that information to our model. We use the confusion matrix to assess the model's performance after it has been implemented effectively. To demonstrate the effectiveness of our model architecture, we will generate bar graphs and scatter plots for each model to display model loss and accuracy. The output of this model is visualized with actual class and predictive class and the result has a graphical representation for each and every output facial Images which makes our recognition system user-friendly.

"The Butcher on the Bus" is Less Familiar to Older Adults than She is to Younger Adults.

The "butcher on the bus" is a term describing recognizing someone as familiar but failing to recollect how we know them. Previous studies probing this phenomenon have not used paradigms that mimic real life, and age-related differences have not been adequately addressed. In two studies, younger and older adults studied faces in scene contexts a variable number of times. In the test phases, studied faces were presented identical to the study phase, new faces were presented with new scenes, or studied faces were presented in a different context. In Study 1, this different context was the face presented with new features (pose, hairstyle, and clothing) and in a new scene. In Study 2, this different context was the same scene, but with new features. Participants made recollect, familiar, or new judgments, as well as source memory judgments for each face. In both studies, younger adults had better recognition discrimination and recollection. The critical finding of both studies was that when faces were presented with new features, in either the same scene or a different scene, older adults did not find these faces as familiar as did younger adults. Age-related declines in face recognition when features change may impact social interactions and everyday recognition tasks, highlighting the need for further research and supportive strategies to assist older adults in recognizing people.

Facial Emotion Recognition Using Residual Neural Networks

Facial Emotion Recognition Using Residual Neural Networks

Better face-name recall is associated with better face recognition ability.

Face recognition is a highly developed and specialised human ability, distinct from other cognitive abilities. Previous studies examining individual differences in face recognition have focused on face perception and specialised perceptual mechanisms such as holistic face processing. However, the contribution of specific face memory processes to face recognition ability remains unclear. In 99 neurotypical individuals, we administered validated face perception assessments, three face memory tasks (old/new task, face-scene task, face-name/occupation task), and the Cambridge Face Memory Test (CFMT) to assess face recognition ability. We found that after accounting for face perception ability (which significantly predicted face recognition ability), face-name recall and recollection of faces in the face-scene task predicted unique variance in face recognition ability, with face-name recall being the strongest predictor. This highlights that associative memory mechanisms contribute to face recognition abilities and suggests that the ability to learn and recall proper names is particularly important to face recognition.

Modeling Of Mask Detection Systems, Distance Between Objects And Facial Recognition Using The Tiny-YOLOV4 Method, Convolutional Neural Network, And Viola Jones

The development of technology is currently increasing very rapidly, the latest technology such as Face Detection and Face Recognition in public facilities has been widely applied, such as cameras for attendance, facial detection devices as mobile phone security and various other new innovations in the field of technology. The purpose of creating this model is to determine the distance, and the use of masks in public places and to determine the performance when the two algorithms between Tiny-YOLOV4 and Viola Jones are combined. This method uses CPU testing with a clock speed of 2.9 GHz and GPU with a clock speed of 1590 MHz with an accuracy level of 92.6% for mask object detection and 90.67% for face recognition with a maximum distance in front of the camera for human detection is 830 centimeters, mask detection 730 centimeters, and face recognition 530 centimeters. The results of the study produced an accuracy of 92.6% for mask detection and an accuracy of 90.67% for face recognition at maximum distances of 730 cm and 530 cm respectively. The conclusion shows that this system is effective for detecting masks, faces, and distance, providing a significant tool for monitoring compliance in public spaces. Implementing this system can serve as a preventive measure for potential future pandemics

Behavioral and neural evidence for difficulty recognizing masked emotional faces.

Facial emotion recognition is vital for human social behavior. During the COVID-19 pandemic, face masks were widely adopted for viral mitigation and remain crucial public health tools. However, questions persist about their impact on emotion recognition and neural processing, especially in children, parents, and young adults. We developed the Masked Affective and Social Cognition task, featuring masked and unmasked faces displaying fear, sadness, and anger. We recruited three racial and ethnically diverse samples: 119 college students, 30 children who entered school age at the beginning of the pandemic, and 31 fathers of the aforementioned children. Of the latter two groups, 41 participants (n = 23 fathers, 18 children) did the Masked Affective and Social Cognition task during a neuroimaging scan, while the remaining 20 participants (n = 8 fathers, 12 children) who were not eligible for scanning completed the task during their lab visit. Behaviorally, we found that participants recognized emotions less accurately when viewing masked faces and also found an interaction of emotion by condition, such that accuracy was particularly compromised by sad masked faces. Neurally, masked faces elicited greater activation in the posterior cingulate, insula, and fusiform gyrus. Anterior insula and inferior frontal gyrus activation were driven by sad, masked faces. These results were consistent across age groups. Among fathers, activation to sad masked faces was associated with stress and depression. Overall, our findings did not depend on previous mask exposure or timing of participation during the pandemic. These results have implications for understanding face emotion recognition, empathy, and socioemotional neurodevelopment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The ethics of facial recognition in police practice: beyond the usual concerns

The ethics of facial recognition in police practice: beyond the usual concerns

Correction: Now you see me, now you don’t: why the UK must ban police facial recognition

Correction: Now you see me, now you don’t: why the UK must ban police facial recognition

SEGURANÇA PÚBLICA, VIGILANTISMO E NOVAS TECNOLOGIAS: O RECONHECIMENTO FACIAL COMO FERRAMENTA DE GARANTIA DE DIREITOS

This study analyzes the use of facial recognition technologies as a public security tool, addressing its benefits and associated ethical and legal challenges. The research highlights that, although the technology can improve efficiency in identifying suspects and solving crimes, especially during large events, significant concerns remain regarding privacy and discrimination. In Brazil, inadequate implementation and the lack of specific regulations reinforce criticisms of the unequal application of the technology, particularly in vulnerable communities. The study suggests that public acceptance of facial recognition depends on transparency, proper oversight, and alignment with legal frameworks, such as the LGPD. Only through clear and ethical governance will it be possible to balance the benefits of public security with the protection of fundamental rights.