Face Database Research Articles

EGEFACE: A new face memory test with static and dynamic images

Face memory is a crucial cognitive ability necessary for maintaining a healthy social life. Recent studies reveal large individual differences in face recognition ability. Face memory tests are used to evaluate this ability. The main purpose of this study was to develop a new face memory test (EGEFACE) addressing the limitations of existing tests using both static and dynamic stimuli to increase ecological validity; employing face recognition algorithms to adjust test difficulty; measuring face memory accuracy independently of response bias by including both target-absent and target-present trials and using ROC analysis; and developing a test to measure both ends of the face recognition ability spectrum. After building a new database of static and dynamic faces, we created three difficulty levels using a face recognition algorithm. We collected data from 703 participants in two steps and examined the internal consistency, split-half reliability, and item–total score correlations. The reliability analysis confirmed that both target-absent and target-present trials of EGEFACE were reliable. High EGEFACE performers scored near super recognizer levels on CFMT+, while low performers showed limited overlap with prosopagnosic-level performance on CFMT+, suggesting EGEFACE’s sensitivity across different levels of face recognition ability. Overall, results indicated a moderate positive correlation between EGEFACE and CFMT+, showing that both tests assess similar cognitive skills, while a low to moderate correlation with KFMT suggests that EGEFACE measures cognitive ability that is related to yet distinct from face perception. The results suggest that EGEFACE shows promise as an ecologically valid and effective alternative tool for assessing individual differences in face memory.

Development and validation of a face database for the recognition of facial expressions of basic emotions in the Brazilian population

BackgroundThe recognition of facial expressions of emotions is an essential skill for social functioning, as it enables recognizing the possible intentions of others.Main body.Cultural context is an important aspect to consider in this skill, as it tends to modulate the recognition of facial expressions.ObjectiveTo develop and validate a set of facial expressions of basic emotions of Brazilian individuals considering the population distribution of the country according to age group, sex, and race.MethodsA procedure with three phases was created to generate basic emotions and photograph facial expressions. A total of 459 Brazilians were then asked to identify the emotions of these facial expressions. Content validity was investigated based on the analysis of specialists and the recognition of emotions by Brazilian individuals.ResultsThe final database consists of 56 high-quality color images. A good level of agreement was found for the expressions perceived by the assessors (81.6%).ConclusionThe percentage of correct recognition of each emotion and the characteristics of the models are presented so that future studies can choose the most adequate images to meet their specific needs.

A New Approach to Recognize Faces Amidst Challenges: Fusion Between the Opposite Frequencies of the Multi-Resolution Features

This paper proposes a new approach to pixel-level fusion using the opposite frequency from the discrete wavelet transform with Gaussian or Difference of Gaussian. The low-frequency from discrete wavelet transform sub-band was fused with the Difference of Gaussian, while the high-frequency sub-bands were fused with Gaussian. The final fusion was reconstructed using an inverse discrete wavelet transform into one enhanced reconstructed image. These enhanced images were utilized to improve recognition performance in the face recognition system. The proposed method was tested against benchmark face datasets such as The Database of Faces (AT&T), the Extended Yale B Face Dataset, the BeautyREC Face Dataset, and the FEI Face Dataset. The results showed that our proposed method was robust and accurate against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with time gap, and conditions where the person uses attributes such as glasses. The proposed method is comparable to state-of-the-art methods and generates high recognition performance (more than 99% accuracy).

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

The contribution of facial recognition technology to wrongful arrests and trauma.

When a crime is captured on video, law enforcement agencies increasingly have used facial recognition technology (FRT) to generate suspects to investigate. However, there are increasing examples of people who have been wrongfully arrested based because of the inaccurate results returned from these artificial intelligence-assisted searches of facial databases, despite very low error rates in the accuracy of these systems. We discuss the reliability of the evidence provided by a match returned by FRT, propose a framework for identifying potential problems with the use of FRT in criminal investigations, and review the research on the general trauma that comes from justice system involvement, trauma that is compounded by wrongful arrest and conviction. We also provide an analysis of how database size affects the evidentiary value of the matches returned by FRT. Variables like facial database size, race of the culprit, and quality of the probe photo can increase the likelihood that FRT systems will return false positive matches. The use of FRT for developing suspects in criminal investigations is likely to exacerbate the already profound racial disparities in the outcomes produced by the criminal legal system and increase trauma experienced by those who are wrongfully arrested or convicted. We recommend extreme caution surrounding its use. In addition, we call for more research on the trauma associated with wrongful arrest, which is likely to occur with the current use of FRT. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A database of naturalistic expressive faces for studying high arousal states

Recent studies comparing behavior to different sets of facial stimuli have highlighted a need to employ more naturalistic, genuine facial expressions in cognitive research. To address this need, we identified and selected a large set of highly expressive face stimuli from the public domain, and used these stimuli to test whether participants can recognise when others are experiencing pain from their facial behaviour. After identifying 315 expressive faces to represent the kinds of facial behaviours often seen in three distinct contexts (i.e., injury-related, loss-related and victory-related), we ran six behavioural ratings tasks to characterise these faces along six dimensions; level of arousal, emotional valence, level of physical pain, attractiveness, familiarity, and perceived gender. The results indicate that injury-related expressions are recognised as lower in emotional valence than victory-related expressions, and higher in psychological arousal than both victory- and loss-related expressions. Overall, these findings suggest that the intense, energetic expressions of people in competitive situations are not rendered ambiguous to third parties by increased arousal. These results validate the use of naturalistic facial expressions in studies of non-verbal, injury-related behaviours and their recognition in forensic and clinical settings. PerspectiveHere we created and validated a large set of visual stimuli, which have been made available to the scientific community. Our results demonstrate that among high-arousal states, expressions related to feelings of intense pain and injury are visually distinct from expressions related to loss or triumph. Thus, the Wild Faces Database – High Arousal States (WFD-HAS) extension provides an important tool for understanding how we recognise injury-related facial expressions in the real world.

Considering How Machine‐Learning Algorithms (Re)produce Social Biases in Generated Faces

ABSTRACTAdvances in computer science–specifically in the development and use of generative machine learning–have provided powerful new tools for psychologists to create synthetic human faces as stimuli, which ultimately provide high‐quality photorealistic face images that have many advantages, including reducing typical ethical and privacy concerns and generating face images from minoritized communities that are typically underrepresented in existing face databases. However, there are a number of ways that using machine learning‐based face generation and manipulation software can introduce bias into the research process, thus threatening the validity of studies. The present article provides a summary of how one class of recently popular algorithms for generating faces–generative adversarial networks (GANs)—works, how we control GANs, and where biases (with a particular focus on racial biases) emerge throughout these processes. We discuss recommendations for mitigating these biases, as well as how these concepts manifest in similar modern text‐to‐image algorithms.

Advancements in Multi-Modal Face Recognition: A State-of-the-Art Review of Security Biometrics

This survey is a state-of-the-art review of recent advancements in face recognition, particularly multi-modal face recognition and its associated applications in the domain of security biometrics and identity verification. In this regard, the paper sheds light on the significance of the Sejong Face database and several other leading databases as equally significant tools in studying some of the most challenging tasks in face recognition: occluded and diverse face identification, cross-modality evaluation, and infrared-visible face detection. It also insists on the high challenge of face identification across scenarios and with different forms of disguise. In addition, it emphasizes the need for an effective security framework to be implemented with other major sectors that deal in finances. The purpose is to develop an exhaustive list of methodologies and innovations based on the Sejong Face Database with the intention of finding new methods and algorithms that guarantee accuracy and reliability. Specifically, the application of deep learning methods comprising convolutional neural networks with cross-modality discriminator networks and unit-class loss demonstrates significant advancements in the development of face recognition systems; consequently, these systems exhibit enhanced security and efficacy. The review article is an important source that points out the current status of the research, as well as future possibilities, regarding biometric authentication and its implications in the digital scenario.

Facial expression morphing: enhancing visual fidelity and preserving facial details in CycleGAN-based expression synthesis.

Recent advancements in facial expression synthesis using deep learning, particularly with Cycle-Consistent Adversarial Networks (CycleGAN), have led to impressive results. However, a critical challenge persists: the generated expressions often lack the sharpness and fine details of the original face, such as freckles, moles, or birthmarks. To address this issue, we introduce the Facial Expression Morphing (FEM) algorithm, a novel post-processing method designed to enhance the visual fidelity of CycleGAN-based outputs. The FEM method blends the input image with the generated expression, prioritizing the preservation of crucial facial details. We experimented with our method on the Radboud Faces Database (RafD) and evaluated employing the Fréchet Inception Distance (FID) standard benchmark for image-to-image translation and introducing a new metric, FSD (Facial Similarity Distance), to specifically measure the similarity between translated and real images. Our comprehensive analysis of CycleGAN, UNet Vision Transformer cycle-consistent GAN versions 1 (UVCGANv1) and 2 (UVCGANv2) reveals a substantial enhancement in image clarity and preservation of intricate details. The average FID score of 31.92 achieved by our models represents a remarkable 50% reduction compared to the previous state-of-the-art model's score of 63.82, showcasing the significant advancements made in this domain. This substantial enhancement in image quality is further supported by our proposed FSD metric, which shows a closer resemblance between FEM-processed images and the original faces.

Hawai‘i face database: a racially and ethnically diverse set of facial stimuli

ABSTRACT Within psychology, face perception processes have been widely studied, examining traits and social categories that a face can communicate. However, much of this research has predominately focused on White faces. We review existing face databases that include racially diverse stimuli and note the lack of representation of Asian subgroups (e.g. East, South, and Southeast Asian), Pacific Islanders, as well as both Multiracial and multiethnic faces (especially with multiple minoritized backgrounds). We provide a new racially diverse set of free, standardized images including 140 unique faces representing eight different groups that vary in ethnicity and race (Asian, East Asian, Southeast Asian, Pacific Islander/Native Hawaiian, Hispanic/Latinx, White, Multiracial, and Multiracial Asian), along with norming data. These images and data are available for access use in research: https://osf.io/fkn7y

Children with facial differences experience deficits in emotion skills.

Children with facial differences, such as repaired cleft lip and palate (CLP), may present with reduced capacity for sensorimotor simulation, particularly in the form of facial mimicry. This study examined whether facial mimicry, emotion recognition, and empathy skills are reduced in children with CLP when compared with sex/age-matched controls. A case-control design was utilized. Forty-five children between the ages of 8 and 12 with CLP, and 45 age/sex-matched controls were recruited. Participants completed a facial mimicry task, and facial movements were tracked and quantified using OpenFace. Participants also completed picture and context-based emotion recognition tasks. Picture-based assessment involved identifying emotions from the Dynamic FACES database. Context-based assessment consisted of identifying how a child might feel in various situations. Finally, participants and their parents completed the Empathy Questionnaire for Children and Adolescents (EmQue-CA). Children with CLP presented with significantly reduced facial mimicry (p = .017), picture-based (p < .001), and context-based emotion recognition scores (p < .001) when compared with controls. Better facial mimicry was associated with better picture-based emotion recognition scores in the control group only (r = .22, p < .01). Children with CLP also had significantly lower child and parent-proxy EmQue-CA scores (p < .001). Greater facial mimicry significantly predicted better parent-proxy EmQue-CA scores (p = .016) but did not predict child scores. Children with CLP presented with reduced facial mimicry, poorer emotion recognition, and empathy skills. These findings have a broader relevance as they suggest children with facial differences may present with reduced facial mimicry and/or deficits in emotion recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Face Recognition Using The Subspace and Deep Learning Algorithms For Cases of Sufficient and Insufficient Data

In face recognition, the distance criterion significantly influences the recognition rate. Misclassified test signals can be accurately reassigned to the correct class using various distance measures and the nearest neighbor algorithm. This study uniquely explores the recognition performance of DCVA, Fisherface subspace classifiers, and Convolutional Neural Network (CNN) in face recognition, an aspect not thoroughly explored in the literature. Accordingly, this study introduces a Discriminative Common Vector-based (DCVA) algorithm utilizing various distance measures for face recognition for the first time. Additionally, the Fisherface-based algorithm uses different distance measures and nearest neighbors. Experiments were conducted on three different face databases. The images were downsampled to simulate both sufficient and insufficient data conditions. Experimental results indicate that the Correlation distance measure generally outperforms the Euclidean distance for the DCVA and Fisherface-KNN algorithms under both data conditions. The Fisherface-KNN algorithm surpasses the classical Fisherface in performance for various distance measures and nearest neighbor numbers and yields better recognition rates than the DCVA algorithm in sufficient data conditions. Moreover, while DCVA and Fisherface-KNN achieved superior results for two smaller face databases, CNN demonstrated better performance for larger databases.

Diversity-driven Privacy Protection Masks Against Unauthorized Face Recognition

Face recognition (FR) technologies have enabled many life-enriching applications but have also opened doors for potential misuse. Governments, private companies, or even individuals can scrape the web, collect facial images, and build a face database to fuel the FR system to identify human faces without their consent. This paper introduces PMask to combat such a privacy threat against unauthorized FR. It provides a holistic approach to enable privacy-preserving sharing of facial images. PMask preprocesses the facial image and hides its unique facial signature through iterative optimization with dual goals: (i) minimizing the amount of noise to ensure high image quality and (ii) minimizing the perception loss between the privacy-protected face and the original face to ensure the face is recognizable to be the same person by humans. Extensive experiments are conducted on eight representative FR models to evaluate PMask against unauthorized FR. The results validate that PMask provides much stronger protection, introduces less perceptible changes to facial images, and runs faster than state-of-the-art methods to provide privacy protection with a better user experience.

Validity Study of an Emotional Face-Database in Iranian Community

Objective: To investigate the accuracy of facial emotion recognition in the Iranian community, a face database validated in this community is required. To do this, we conducted a validation study on the Radboud face database. The primary objective of this study was to evaluate the accuracy of recognizing emotions through faces in an Iranian sample and then to choose the pictures with high agreement in terms of detecting emotions. Method: This is a cross sectional study recruiting a total number of 142 males and females aged between 20 and 50 years old (Mean ± SD of age 31.7 ± 7.07). The participants were instructed to detect the type of emotion of each face as well as its valence and arousal. The percentage of participants’ agreement on evaluating each picture was assessed. To evaluate the effect of different variables on participants’ accuracy, one way and repeated measure ANOVA analyses were also used. Results: Emotional faces were recognized by around 84% of the participants. The highest accuracy belongs to happy (Mean ± SD of 98 ± 6.1%) and the lowest one to neutral (75 ± 18.06%) faces. The accuracy for detecting other emotions were as follows: sad (91 ± 8.7%), surprised (87 ± 10.64%), angry (77 ± 15.6%), and fearful (76 ± 15.26%). Additionally, we found no differences between male and female participants in terms of recognizing emotions. Then we selected the pictures with high agreement (above 85 percent) in labeling emotions among the participants. Conclusion: The current study provided a valid emotional face database based on Iranian participants’ responses in terms of recognizing basic emotions. The selected pictures can be used in designing tasks to evaluate emotion recognition ability in clinical and nonclinical populations. It can also be used in designing applications to improve detecting emotion in clinical samples such as individuals with autism spectrum disorder.

Long-term photogrammetric outcomes of midface advancement in Apert syndrome: are we nearing normal?

The aesthetic goals of midface surgery in Apert syndrome are to correct the multi-planar midface deficiency and normalize facial ratios. This study characterizes the long-term photogrammetric outcomes of midface advancement in Apert syndrome. Patients with Apert syndrome who underwent midface distraction from 2000 to 2023 were retrospectively reviewed. Soft tissue measurements were applied to preoperative (T0), short-term postoperative (T1), and long-term postoperative (T2) photographs. Long-term changes were compared between subcranial and transcranial procedures, segmental and non-segmental osteotomies, and individual techniques. Frontal facial dimensions at time T2 were compared to age- and sex-matched normal controls from the NIMH-ChEFS face database. Twenty patients had postoperative follow-up of T1 = 0.6 ± 0.4 and T2 = 4.7 ± 1.1years and were compared to 40 normal controls. From time T0 to T2, middle facial third height increased (26.1 ± 3.0% to 27.8 ± 2.6%, p = 0.026), canthal tilt improved (- 7.6° ± 3.7° to - 3.9° ± 4.4°, p < 0.001), and facial convexity increased (182.9° ± 6.2° to 167.9° ± 8.6°, p < 0.001). From time T1 to T2, facial convexity flattened (159.4° ± 10.1° to 167.9° ± 8.6°, p < 0.001). Compared to controls, patients at time T2 had shorter middle facial third height (27.8 ± 2.6% vs. 32.0 ± 1.9%, p < 0.001) and reverse canthal tilt (- 3.9° ± 4.4° vs. 5.4° ± 2.6°, p < 0.001). A tailored treatment approach to the Apert midface yields varying degrees of resolution of central midfacial shortening, canthal tilt, and facial concavity at 5years postoperatively. A slight reduction in facial convexity over time, likely reflecting pseudorelapse in the setting of sagittal overcorrection, should be anticipated. Greater utilization of segmental osteotomies may bring facial third ratios and canthal tilt closer to normal morphometric values.

Altered emotion perception in insomnia, anxiety, depression, mania, psychotic experiences and schizotypal symptoms: a dataset

This data resource provides evidence concerning the prevalence of perceptual alterations of emotional faces amongst individuals experiencing symptoms of insomnia, anxiety, depression, mania, psychotic experiences, and schizotypal tendencies. More specifically, we explored the categorisation accuracy (whether the displayed emotion was correctly identified), misperception (which emotion an incorrect judgment was perceived to be), intensity (extent of the emotion signal strength) and emotional valence (the extent and direction of perceived affect) of six facial expressions of emotion from the Karolinska Directed Emotional Faces database. Complete data from N = 572 respondents are included. The dataset is available to other researchers and is provided on Figshare. Information concerning the data records, usage notes, code availability and technical validation are presented. Finally, we present demographic and correlational data concerning psychiatric symptoms and alterations in the perception of emotional faces.

Quarantine Order Violators System using Face Recognition (FACID)

Face recognition technology is commonly utilized for user authentication and verification by analysing a digital image of a person's face and matching it against a database of faces for identification purposes. The COVID-19 pandemic has led to mandatory quarantines and the use of quarantine bracelets for some individuals, which can be time-consuming and require a lot of effort. Face recognition technology can help raise awareness about the current situation and alleviate some of the burden associated with quarantine measures. The aim of this research is to concentrate on the face recognition of individuals with a history of quarantine, as a measure to prevent the spread of COVID-19 in educational institutions like universities, colleges, and schools. This research study concentrates on individuals with a history of quarantine orders. The system will employ the Histograms of Oriented Gradients (HOG) algorithm for face detection. Additionally, the system will utilize the Face Landmark Algorithm to compare the 128-d vector with images stored locally. The system will make use of the Helen face collection dataset for its data requirements. The aim of this study is to explore and identify techniques for detecting individuals who violate quarantine measures and issuing notifications through the proposed system. By implementing this system, it could contribute to creating a more secure environment within the educational institution and potentially reduce the spread of the virus.

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers.

Paper is a readily available material in nature. Its recyclability, eco-friendliness, portability, flexibility, and affordability make it a favored substrate for researchers seeking cost-effective solutions. Electronic devices based on solution process are fabricated on paper and banknotes using PVK and SnO2 nanoparticles. The devices manufactured on paper substrates exhibit photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO2 nanoparticles. A light-modulated artificial synapse device is realized on a paper at a low voltage bias of -0.01V, with an average recognition rate of 91.7% based on the Yale Face Database. As a security device on a banknote, 400 devices in a 20 × 20 array configuration exhibited random electrical characteristics owing to the local morphology of the SnO2 nanoparticles and differences in the depletion layer width at the SnO2/PVK interface. The security Physically Unclonable Functions (PUF) key based on the current distribution extracted at -1V show unpredictable reproducibility with 50% uniformity, 48.7% inter-Hamming distance, and 50.1% bit-aliasing rates. Moreover, the device maintained its properties for more than 210 days under a curvature radius of 8.75mm and bias and UV irradiation stress conditions.

Are contemporary facial recognition algorithms making human facial comparison performance worse?

Facial recognition plays a vital role in several security and law enforcement workflows, such as passport control and criminal investigations. The identification process typically involves a facial recognition system comparing an image against a large database of faces to return a list of probable matches, called a candidate list, for review. A human then looks at the returned images to determine whether there is a match. Most evaluations of these systems tend to examine the performance of the algorithm or human in isolation, not accounting for the interaction that occurs in operational contexts. To ensure optimal whole system performance, it is important to understand how the output produced by an algorithm can impact human performance. Anecdotal claims have been made by users of facial recognition systems that the images being returned by new algorithms in these systems have become more similar in appearance compared to old algorithms, making their job of determining the presence of a match more difficult. This paper explores whether these claims are true and whether the latest facial recognition algorithms decrease human performance compared to an old algorithm from the same company. We examined the performance of 40 novice participants on 120 face matching trials. Each trial required the participant to compare a face image against a candidate list containing eight possible matches returned by either a new or old algorithm (60 trials of each). Overall, participants were more likely to make errors when presented with a candidate list from a new algorithm. Specifically, they were more likely to misidentify an incorrect identity as a match. Participants were more accurate, confident, and faster on candidate lists from the older algorithm. These findings suggest that new algorithms are generating more plausible matches, making the task of determining a match harder for humans. We propose strategies to potentially improve performance and recommendations for future research.

Facial Expression Recognition Using a Semantic-Based Bottleneck Attention Module

Facial expression recognition (FER) is of great value in affective computing. Currently, many FER models have difficulty distinguishing between primary and secondary features. To this end, we propose a FER model based on the semantic bottleneck attention module (BAM). The improvement is that the input image is processed through two branches. BAM uses spatial attention to construct feature maps that can highlight important feature areas and suppress secondary features. The moving flipped bottleneck convolution (MBConv) module introduces random depth, reducing training time and improving model performance. Random depth enables the model to reduce the risk of overfitting during training by randomly skipping some layers. Global average pooling replaces the fully connected layer and compresses the entire feature map (FM) into numerical values to obtain high-purity image features. Through experiments on Extended Cohn-Kanade, Static Facial Expressions in the Wild, and Real-world Affective Faces Database CK+, SFEW, and RAFDB databases, the accuracy of our model reaches 98.33%, 61.14%, and 89.43% respectively, which is better than the other three advanced comparison methods.