Face Identification Research Articles

Face Detection Performance Using CNNs and Bug Bonuty Program (BBP)

Face Detection Performance Using CNNs and Bug Bonuty Program (BBP)

Exploring Convolutional Neural Networks for Facial Expression Recognition: A Comprehensive Survey

Facial emotion recognition is a very sought-after field in recent times as it has many important applications such as effective calculations, video game testing and motion capture in video games, human-computer interaction through machine vision, computer research etc. Facial expression is considered a nonverbal form of communication, as it reveals an individual's internal sentiments and emotional states through changes in multiple facial landmark points. Facial identification provides a more comprehensive insight into the person's thoughts and these expressions are analyzed using deep learning methods, such as CNN. The accuracy rates achieved are compared to other methods. In this paper, a concise exploration of diverse applications within Facial Expression Recognition (FER) fields and the publicly accessible data sets employed in FER studies is outlined. FER using multiple different CNN algorithms is also presented. Finally, through comparing multiple different studies of various CNN algorithms, a table and a chart are provided for a better understanding of the rate of accuracy achieved throughout the use of different datasets.

Appearance debiased gaze estimation via stochastic subject-wise adversarial learning

Recently, appearance-based gaze estimation has been attracting attention in computer vision, and remarkable improvements have been achieved using various deep learning techniques. Despite such progress, most methods aim to infer gaze vectors from images directly, which causes overfitting to person-specific appearance factors. In this paper, we address these challenges and propose a novel framework: Stochastic subject-wise Adversarial gaZE learning (SAZE), which trains a network to generalize the appearance of subjects. We design a Face generalization Network (Fgen-Net) using a face-to-gaze encoder and face identity classifier and a proposed adversarial loss. The proposed loss generalizes face appearance factors so that the identity classifier inferences a uniform probability distribution. In addition, the Fgen-Net is trained by a learning mechanism that optimizes the network by reselecting a subset of subjects at every training step to avoid overfitting. Our experimental results verify the robustness of the method in that it yields state-of-the-art performance, achieving 3.89°and 4.42°on the MPIIFaceGaze and EyeDiap datasets, respectively. Furthermore, we demonstrate the positive generalization effect by conducting further experiments using face images involving different styles generated from the generative model.

Both identity and non-identity face perception tasks predict developmental prosopagnosia and face recognition ability

Developmental prosopagnosia (DP) is characterised by deficits in face identification. However, there is debate about whether these deficits are primarily perceptual, and whether they extend to other face processing tasks (e.g., identifying emotion, age, and gender; detecting faces in scenes). In this study, 30 participants with DP and 75 controls completed a battery of eight tasks assessing four domains of face perception (identity; emotion; age and gender; face detection). The DP group performed worse than the control group on both identity perception tasks, and one task from each other domain. Both identity perception tests uniquely predicted DP/control group membership, and performance on two measures of face memory. These findings suggest that deficits in DP may arise from issues with face perception. Some non-identity tasks also predicted DP/control group membership and face memory, even when face identity perception was accounted for. Gender perception and speed of face detection consistently predicted unique variance in group membership and face memory; several other tasks were only associated with some measures of face recognition ability. These findings indicate that face perception deficits in DP may extend beyond identity perception. However, the associations between tasks may also reflect subtle aspects of task demands or stimuli.

Human face identification after plastic surgery using SURF, Multi-KNN and BPNN techniques

Facial identification for surgical and non-surgical datasets is getting popular. The reason behind this popularity is the growing need of a robust facial recognition system which is consistent to occlusion, spoofing attacks and last but most important plastic surgery effects. Plastic therapies are undertaken by individuals to beautify their external appearance but it is also undertaken by impostors to commit crimes and falsify their true identities. This research work aims at developing a facial recognition system which can identify genuine and impostor pairs. The projected methodology optimizes face detection via Back-Propagation Neural Network (BPNN) and dimensionality reduction by means of Speeded Up Robust Features followed by Multi-K-Nearest-Neighbor technique. The novelty is the production of an innovative-fangled T-Database which trains the BPNN. Thus, BPNN converges faster and achieves higher recognition. The proposed scheme is not applied till date on a medically altered dataset. We have applied five distance metrics and integrated them to acquire T-Dataset, which is fed to the BPNN. This scheme is tested on surgical and non-surgical datasets and it is deduced that higher recognition is achieved with non-surgical databases as compared to surgical ones. For both surgical and non-surgical datasets, the computational cost attained is the modest.

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

Identity recognition from faces and bodies in schizophrenia spectrum disorders

Deficits in facial identity recognition and its association with poor social functioning are well documented in schizophrenia, but none of these studies have assessed the role of the body in these processes. Recent research in healthy populations shows that the body is also an important source of information in identity recognition, and the current study aimed to thoroughly examine identity recognition from both faces and bodies in schizophrenia. Sixty-five individuals with schizophrenia and forty-nine healthy controls completed three conditions of an identity matching task in which they attempted to match unidentified persons in unedited photos of faces and bodies, edited photos showing faces only, or edited photos showing bodies only. Results revealed global deficits in identity recognition in individuals with schizophrenia (ηp2 = 0.068), but both groups showed better recognition from bodies alone as compared to faces alone (ηp2 = 0.573), suggesting that the ability to extract useful information from bodies when identifying persons may remain partially preserved in schizophrenia. Further research is necessary to understand the relationship between face/body processing, identity recognition, and functional outcomes in individuals with schizophrenia-spectrum disorders.

Recognizing newly learned faces across changes in age

ABSTRACT We examined how well faces can be recognized despite substantial age-related changes, using three behavioural experiments plus Mileva et al.’s (2020, Facial identity across the lifespan. Cognitive Psychology, 116, 101260) PCA + LDA computational model of face recognition. Participants and the model were trained on a set of faces at one age (with each facial identity depicted in multiple images) and tested on their ability to recognize those individuals in images taken at a different age. The younger images were aged 20–30 years and the older images were either 20 or 40 years older. The computational model showed high accuracy, but it performed better if faces were learnt in their younger versions and testing was with the older images than vice versa. The humans did not show this age-direction effect. Although their recognition of faces across either a 20- or 40- year age gap was poor, it was significantly above chance, suggesting that we can extract identity diagnostic information despite substantial changes in outward appearance.

Autistic Individuals Do Not Alter Visual Processing Strategy During Encoding Versus Recognition of Faces: A Hidden Markov Modeling Approach.

Visual face recognition-the ability to encode, discriminate, and recognize the faces of others-is fundamentally supported by eye movements and is a common source of difficulty for autistic individuals. We aimed to evaluate how visual processing strategies (i.e., eye movement patterns) directly support encoding and recognition of faces in autistic and neurotypical (NT) individuals. We used a hidden Markov modeling approach to evaluate the spatiotemporal dynamics of eye movements in autistic (n = 15) and neurotypical (NT) adolescents (n = 17) during a face identity recognition task. We discovered distinct eye movement patterns among all participants, which included a focused and exploratory strategy. When evaluating change in visual processing strategy across encoding and recognition phases, autistic individuals did not shift their eye movement patterns like their NT peers, who shifted to a more exploratory visual processing strategy during recognition. These findings suggest that autistic individuals do not modulate their visual processing strategy across encoding and recognition of faces, which may be an indicator of less efficient face processing.

Facial expressions and identities recognition in Parkinson disease

Parkinson's Disease (PD) is associated with motor and non-motor symptoms. Among the latter are deficits in matching, identification, and recognition of emotional facial expressions. On one hand, this deficit has been attributed to a dysfunction in emotion processing. Another explanation (which does not exclude the former) links this deficit with reduced facial expressiveness in these patients, which prevents them from properly understanding or embodying emotions. To disentangle the specific contribution of emotion comprehension and that of facial expression processing in PD's observed deficit with emotions we performed two experiments on non-emotional facial expressions. In Experiment 1, a group of PD patients and a group of Healthy Controls (HC) underwent a task of non-emotional expression recognition in faces of different identity and a task of identity recognition in faces with different expression. No differences were observed between the two groups in accuracies. In Experiment 2, PD patients and Healthy Controls underwent a task where they had to recognize the identity of faces encoded through a non-emotional facial expression, through a rigid head movement, or as neutral. Again, no group differences were observed. In none of the two experiments hypomimia scores had a specific effect on expression processing. We conclude that in PD patients the observed impairment with emotional expressions is likely due to a specific deficit for emotions to a greater extent than for facial expressivity processing.

Evaluating text classification with explainable artificial intelligence

<span lang="EN-US">Nowadays, artificial intelligence (AI) in general and machine learning techniques in particular has been widely employed in automated systems. Increasing complexity of these machine learning based systems have consequently given rise to blackbox models that are typically not understandable or explainable by humans. There is a need to understand the logic and reason behind these automated decision-making black box models as they are involved in our day-to-day activities such as driving, facial recognition identity systems, online recruitment. Explainable artificial intelligence (XAI) is an evolving field that makes it possible for humans to evaluate machine learning models for their correctness, fairness, and reliability. We extend our previous research work and perform a detailed analysis of the model created for text classification and sentiment analysis using a popular Explainable AI tool named local interpretable model agnostic explanations (LIME). The results verify that it is essential to evaluate machine learning models using explainable AI tools as accuracy and other related metrics does not ensure the correctness, fairness, and reliability of the model. We also present the comparison of explainability and interpretability of various machine learning algorithms using LIME. </span>

Genetic algorithms reveal identity independent representation of emotional expressions.

People readily and automatically process facial emotion and identity, and it has been reported that these cues are processed both dependently and independently. However, this question of identity independent encoding of emotions has only been examined using posed, often exaggerated expressions of emotion, that do not account for the substantial individual differences in emotion recognition. In this study, we ask whether people's unique beliefs of how emotions should be reflected in facial expressions depend on the identity of the face. To do this, we employed a genetic algorithm where participants created facial expressions to represent different emotions. Participants generated facial expressions of anger, fear, happiness, and sadness, on two different identities. Facial features were controlled by manipulating a set of weights, allowing us to probe the exact positions of faces in high-dimensional expression space. We found that participants created facial expressions belonging to each identity in a similar space that was unique to the participant, for angry, fearful, and happy expressions, but not sad. However, using a machine learning algorithm that examined the positions of faces in expression space, we also found systematic differences between the two identities' expressions across participants. This suggests that participants' beliefs of how an emotion should be reflected in a facial expression are unique to them and identity independent, although there are also some systematic differences in the facial expressions between two identities that are common across all individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Self-reference Effect Can Modulate Language Syntactic Processing Even Without Explicit Awareness: An Electroencephalography Study.

Although it is well established that self-related information can rapidly capture our attention and bias cognitive functioning, whether this self-bias can affect language processing remains largely unknown. In addition, there is an ongoing debate as to the functional independence of language processes, notably regarding the syntactic domain. Hence, this study investigated the influence of self-related content on syntactic speech processing. Participants listened to sentences that could contain morphosyntactic anomalies while the masked face identity (self, friend, or unknown faces) was presented for 16 msec preceding the critical word. The language-related ERP components (left anterior negativity [LAN] and P600) appeared for all identity conditions. However, the largest LAN effect followed by a reduced P600 effect was observed for self-faces, whereas a larger LAN with no reduction of the P600 was found for friend faces compared with unknown faces. These data suggest that both early and late syntactic processes can be modulated by self-related content. In addition, alpha power was more suppressed over the left inferior frontal gyrus only when self-faces appeared before the critical word. This may reflect higher semantic demands concomitant to early syntactic operations (around 150-550 msec). Our data also provide further evidence of self-specific response, as reflected by the N250 component. Collectively, our results suggest that identity-related information is rapidly decoded from facial stimuli and may impact core linguistic processes, supporting an interactive view of syntactic processing. This study provides evidence that the self-reference effect can be extended to syntactic processing.

Trait inferences from the “big two” produce gendered expectations of facial features

Prescriptive stereotypes based on, respectively, agency and communality reflect how people expect men and women to behave. Deviating from such prescriptions limits opportunities for men and women in ways that reinforce traditional gender roles. In the current work, we examine whether people have expectations of gendered facial features based on agentic and communal descriptions of targets and if these expectations extend to who people think is best suited for workplace tasks. Across five experiments, people expected more facial masculinity for targets paired with agentic relative to communal traits (Experiments 1, 2a-b) and workplace behaviors (Experiments 3a-b). This expectation effect emerged when gendered facial features (e.g., more masculinized and feminized versions of face identities) were manipulated across (Experiment 1) and within (Experiments 2a-b, 3a-b) gender, regardless of whether traits were explicitly stated (Experiments 1, 2a-b, 3a) or inferred (Experiment 3b), and regardless of trait valence. When people made decisions about two same-gender faces, the gender of those faces accentuated trait effects. More masculine male (relative to female) faces were consistently expected more for agentic traits and workplace tasks, but consistently expected less for communal traits and workplace tasks (Experiments 2a, 3a-b). We then conceptually replicated expectation effects by showing that mental representations of agentic and communal faces appear correspondingly gendered (Experiment 4). Finally, we provide exploratory analyses showing that expectation effects may differentially vary by perceiver gender across contexts. These findings illustrate a non-verbal route by which people make decisions based on gender stereotypes that have wide-ranging implications for workplace behavior.

Framework for the Classification of Facial Emotions Using Soft Computing Techniques

Background:: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods:: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results:: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion:: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

Parental burnout, coparenting, and internalized sexual stigma during COVID-19 in parents with minoritized sexual identities.

The COVID-19 pandemic acted as a model of stressful situations for parents insofar as it led to unprecedented difficulties in childcare and caregiving, resulting in increased levels of parental burnout, worldwide. To date, research on parental burnout has mainly involved heterosexual parents. However, parents with minoritized sexual identities face partially different stressors, including internalized sexual stigma, and they also have partially different resources, including a more egalitarian division of childcare labor. Between April 2020 and February 2021, 32 lesbian mother families by donor insemination (n = 64 lesbian mothers) and 28 gay father families by gestational surrogacy (n = 56 gay fathers), all with a child aged 6-10 years and living in Italy, were recruited. In each family, both parents self-rated their parental burnout, coparenting, and internalized sexual stigma. Multilevel modeling indicated that lesbian mothers reported greater parental burnout than gay fathers. Moreover, lower coparenting quality was associated with greater parental burnout. Finally, internalized sexual stigma had a significant both direct and interactive effect on parental burnout, with higher levels of internalized sexual stigma resulting in greater parental burnout, especially in gay fathers. Considering the sexual minority stress theory and the risks and resources balance theory, the results indicate the importance of preventing and treating parental burnout in lesbian and gay parents by focusing on their internalized sexual stigma and coparenting relationship. Also, incorporating the positive psychology framework in future research would help identify in these parents the resources deriving from their minoritized sexual identities to deal with parental burnout. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pattern Recognition in Embedded Systems for Event Occurrences

Abstract: PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN)suggests two deep face detection methods. These are presented to use the deep learning algorithm.This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.

A proposed reconstruction method of a 3D animation scene based on a fuzzy long and short-term memory algorithm.

With the development of computer technology leading to a broad range of virtual technology implementations, the construction of virtual tasks has become highly demanded and has increased rapidly, especially in animation scenes. Constructing three-dimensional (3D) animation characters utilizing properties of actual characters could provide users with immersive experiences. However, a 3D face reconstruction (3DFR) utilizing a single image is a very demanding operation in computer graphics and vision. In addition, limited 3D face data sets reduce the performance improvement of the proposed approaches, causing a lack of robustness. When datasets are large, face recognition, transformation, and animation implementations are relatively practical. However, some reconstruction methods only consider the one-to-one processes without considering the correlations or differences in the input images, resulting in models lacking information related to face identity or being overly sensitive to face pose. A face model composed of a convolutional neural network (CNN) regresses 3D deformable model coefficients for 3DFR and alignment tasks. The manuscript proposes a reconstruction method for 3D animation scenes employing fuzzy LSMT-CNN (FLSMT-CNN). Multiple collected images are employed to reconstruct 3D animation characters. First, the serialized images are processed by the proposed method to extract the features of face parameters and then improve the conventional deformable face modeling (3DFDM). Afterward, the 3DFDM is utilized to reconstruct animation characters, and finally, high-precision reconstructions of 3D faces are achieved. The FLSMT-CNN has enhanced both the precision and strength of the reconstructed 3D animation characters, which provides more opportunities to be applied to other animation scenes.

Facial Recognition System with Attendance Tracking Using Python and Open CV

In educational institutions, businesses, and offices, attendance tracking stands as a crucial daily task. Traditionally performed manually, be it through name calling or roll numbers, there is a need for a more efficient solution. This project aims to revolutionize attendance management by implementing a Face Recognition-based system. Installed in classrooms, the system captures student details like name, roll number, class, section, and photographs, utilizing OpenCV for image extraction. As students approach the device, it takes and compares pictures with a trained dataset. The process involves face identification with a Haar cascade classifier, recognition using LBPH and automatic attendance labeling. An Excel sheet is generated and updated hourly, incorporating information from the class instructor. This approach aligns with the demand for modernization and efficient time management in attendance procedures.

Disentangled deep generative models reveal coding principles of the human face processing network.

Despite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently, deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that "disentangles" different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model's learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigate the representation of different latent dimensions across face-selective voxels. We find that low- and high-level face features are represented in posterior and anterior face-selective regions, respectively, corroborating prior models of human face recognition. Interestingly, though, we find identity-relevant and irrelevant face features across the face processing network. Finally, we provide new insight into the few "entangled" (uninterpretable) dimensions in our model by showing that they match responses in the ventral stream and carry information about facial identity. Disentangled face encoding models provide an exciting alternative to standard "black box" deep learning approaches for modeling and interpreting human brain data.