Face Images Research Articles

Cutting speed and behaviors of ice using Yb-doped fiber laser

The use of a laser to cut or drill ice has been proposed and demonstrated multiple times in previous decades as a novel, but never adopted, machining tool in glaciology and paleoclimate studies. However, with the rapid development of high power fiber-laser technology over the past few decades, it is timely to perform further studies using this new tool. An investigation is made herein on the cutting of ice using a Yb-doped fiber laser emitting at a wavelength of 1070 nm, the most extensively developed and highest power fiber laser technology, in pulsed and continuous-wave operation. Visible-light observations of clear tap water ice samples, moving at a constant velocity relative to a pulsed laser beam, demonstrate a linear relationship between the duration of a millisecond-range laser pulse and the depth of the meltwater-free cut formed in response. Thermal imaging of the irradiated face shows that peripheral heating trends linearly for pulse lengths greater than 5 ms. A comparison of pulse trains with a constant time-averaged power suggests that shorter pulses are advantageous in slot-cutting efficiency and in minimizing visible alterations in the surrounding ice. These results demonstrate the viability of powerful fiber-compatible lasers as a tool for ice sample retrieval and processing.

Quantification of facial symmetry in orthognathic surgery: A novel approach integrating 3D contour maps and hyper-dimensional computing

This study aimed to enhance the evaluation of facial symmetry crucial for planning and assessing outcomes of orthognathic surgery (OGS). An innovative approach combining three-dimensional (3D) facial contour lines with hyperdimensional (HD) computing was developed for this purpose. Data were collected using 3D cone beam computed tomography (CBCT) at Chang Gung Memorial Hospital from 2016 to 2021. A comprehensive dataset was compiled, including images from 150 normal individuals and 2500 patients, totaling 5150 preoperative and postoperative facial images. A machine learning model was trained to analyze these images, and 3D contour data were used to create a facial symmetry quantification system with HD computing. Additionally, 3D CBCT data from 200 patients before and after OGS were retrospectively reviewed for clinical application. The developed facial symmetry algorithm demonstrated an overall accuracy of 84.1 %. Postoperative facial symmetry scores improved significantly, with a mean score increase of 53 %, from 2.40 to 3.63. The study culminated in the creation of a web-based system that leverages HD computing and 3D contour mapping to automate facial symmetry assessment. This system offers a user-friendly interface for rapid and accurate evaluations, facilitating better communication between clinicians and patients.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

Research on face image recognition in smart city based on improved convolutional neural network

Research on face image recognition in smart city based on improved convolutional neural network

A destructive active defense algorithm for deepfake face images

A destructive active defense algorithm for deepfake face images

EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction

Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emotion classification, we applied a series of signal processing and machine learning methods to the EEG data. The maximum classification accuracy ranged from 43.3% to 90.8% over 38 subjects and classification features could be interpreted neurophysiologically. Our EEG data could be used to develop a reliable HCI system because they were acquired in a natural HCI environment. In addition, auxiliary physiological data measured simultaneously with the EEG data also showed plausible results, i.e., electrocardiogram, photoplethysmogram, galvanic skin response, and facial images, which could be utilized for automatic emotion discrimination independently from, as well as together with the EEG data via the fusion of multi-modal physiological datasets.

Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.

Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability. To determine if estimating PA using deep learning-based algorithms results in the same associations with morbidities and genetic variants as human-estimated perceived age. Self-supervised learning (SSL) and deep feature transfer (DFT) deep learning (DL) approaches were trained and tested on human-estimated PAs and their corresponding frontal face images of middle-aged to elderly Dutch participants (n = 2679) from a population-based study in the Netherlands. We compared the DL-estimated PAs with morbidities previously associated with human-estimated PA as well as genetic variants in the gene MC1R; we additionally tested the PA associations with MC1R in a new validation cohort (n = 1158). The DL approaches predicted PA in this population with a mean absolute error of 2.84 years (DFT) and 2.39 years (SSL). In the training-test dataset, we found the same significant (p < 0.05) associations for DL PA with osteoporosis, ARHL, cognition, COPD and cataracts and MC1R, as with human PA. We also found a similar but less significant association for SSL and DFT PAs (0.69 and 0.71 years per allele, p = 0.008 and 0.011, respectively) with MC1R variants in the validation dataset as that found with human, SSL and DFT PAs in the training-test dataset (0.79, 0.78 and 0.71 years per allele respectively; all p < 0.0001). Deep learning methods can automatically estimate PA from facial images with enough accuracy to replicate known links between human-estimated perceived age and several age-related morbidities. Furthermore, DL predicted perceived age associated with MC1R gene variants in a validation cohort. Hence, such DL PA techniques may be used instead of human estimations in perceived age studies thereby reducing time and costs.

Comparative evaluation of three different behavior management techniques among children aged 6-12 years in dental practice: A single-center, double-blind, randomized controlled trial.

Dental anxiety is characterized by distress in anticipation of dental visits, which may result in a child's refusal to undergo treatment and, ultimately, lead to parents' reluctance towards dental care. The aim of this study was to evaluate and compare the effectiveness of the tell-show-do technique, modeling technique and yogic relaxation technique in the reduction of dental anxiety among children aged 6-12 years. The study was an interventional, parallel-group, single-center, double-blind, randomized controlled trial conducted on 120 children who required restorative treatment without the use of local anesthesia. The participants were selected based on specific inclusion and exclusion criteria and were randomly divided into 3 groups: group 1 - tell-show-do technique; group 2 - modeling technique; group 3 - yogic relaxation technique. The dental anxiety levels were evaluated 4 times using both physiological (oxygen saturation and pulse rate) and behavioral parameters (facial image scale (FIS) and the Face, Legs, Activity, Cry, and Consolability scale (FLACC)). The data was assessed by 2 blinded and calibrated specialists. Statistically significant differences were observed in all 4 parameters among the 3 groups. During both the intraoperative and postoperative periods, the oxygen saturation levels were significantly higher in the yogic relaxation technique group. Moreover, the yogic relaxation technique group exhibited lower pulse rates, FIS and FLACC scores compared to the tell-show-do and modeling technique groups. The practice of yoga has a positive influence on the general health of the individual. Consequently, it can be considered one of the alternative behavioral modification techniques for the reduction of dental anxiety in children.

Emotion recognition using cross-modal attention from EEG and facial expression

The fusion of facial expression and Electroencephalogram (EEG) signals in a multi-modal framework is a comprehensive and accurate method for emotion recognition. However, current methods often tend to directly concatenate two modalities, thereby overlooking the interplay between modalities. Furthermore, the selection of single static facial images for extracting facial features neglected the dynamic changes in facial expressions. These limitations have been identified as factors that constrain the accuracy and stability of the model. In this study, an innovative multimodal emotion recognition network was introduced, which using continuous facial expressions and EEG signals. It incorporated the cross-modal attention fusion mechanism to establish robust correlations between modal feature vectors, thereby generating amalgamated vectors with mutual information. Additionally, a Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) was employed to capture spatiotemporal information from facial expression images. The model proposed in this paper was experimentally evaluated on the DEAP and MAHNOB-HCI datasets. Its recognition accuracy was higher than the existing advanced research methods. At the same time, it still performed well in the Leave-One-Subject-Out (LOSO) experiment based on DEAP dataset. The experimental results showed the effectiveness of the proposed model in the task of multimodal emotion recognition.

Modulation of face processing by top-down attention: Insights from early ERP waveforms

The face fusiform area (FFA) plays a pivotal role in face recognition, yet the precise timeline of its activity remains debated. Using EEG, we conducted three experiments to investigate how expectancy-consistent versus expectancy-inconsistent visual stimuli influence processing dynamics. Participants viewed images of faces, houses, and tools (Experiment 1), celebrity faces (Experiment 2), or animal faces (Experiment 3), preceded by a priming question. Notably, both conditions presented identical visual stimulation, ensuring that observed differences stemmed from cognitive processing rather than sensory input. Our results from Experiments 2 and 3 reveal that while the initial 150 ms period, crucial for unconscious face detection, remained unaffected, subsequent processing exhibited a delay of several milliseconds for expectancy-inconsistent stimuli, indicating additional processing time required for unexpected recognition. Importantly, no significant differences were observed in Experiment 1, where less demanding tasks or generic mental imagery were used, suggesting that the priming effect was not as pronounced in this context. These findings underscore the critical role of the period immediately following the first 150 ms in face identification and individuation, highlighting the influence of top-down attention on face recognition dynamics. This study provides novel insights into the temporal dynamics of face processing and the neural mechanisms underlying top-down attentional modulation.

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.

Prior brief meditation reduces distractor inhibition during cognitive interference.

Mindfulness meditation, comprising focused attention and open monitoring meditations, has been shown to enhance performance on cognitive interference tasks. While this enhancement has been considered not to result from distractor inhibition, no empirical evidence has been provided through behavioral data. In this study, we investigated whether 30-min interventions of focused attention and open monitoring meditations could reduce distractor inhibition in 72 meditation-naïve participants divided into focused attention meditation, open monitoring meditation, and control groups. We employed a task set that combined a cognitive interference task with subsequent preference judgment and surprise recognition tasks, utilizing the mere exposure effect paradigm, along with state and trait questionnaires. The mere exposure effect shows that repeated exposure to face images increases one's preference for them. However, this effect is reduced if participants consciously or unconsciously try to inhibit the face images during stimulus processing. In the cognitive interference task, they judged the direction of the letter superimposed on a distractor face image. In the subsequent preference judgment task, they were asked to rate the preference of face images, half of which were presented in the interference task and the remaining half were not presented. We hypothesized that inhibiting face images presented as distractors would lead to a decrease in preference for them. We found that the mere exposure effect was observed in focused attention meditation and open monitoring meditation groups but not in the control group, indicating that compared to the control, focused attention and open monitoring meditations reduce inhibition processes for distractors during cognitive interference tasks. Furthermore, we found a positive correlation between the intensity of the mere exposure effect and state relaxation before the cognitive interference task as well as a negative correlation between the intensity of the mere exposure effect and state anxiety in the focused attention meditation group, but not in the open monitoring meditation group. This suggests that the processes of reducing inhibition in focused attention and open monitoring meditations differ. Our findings contribute to understanding the attentional mechanisms underlying mindfulness meditation during cognitive interference.

Cross-Subject EEG Feedback for Implicit Image Generation.

Generative models are powerful tools for producing novel information by learning from example data. However, the current approaches require explicit manual input to steer generative models to match human goals. Furthermore, how these models would integrate implicit, diverse feedback and goals of multiple users remains largely unexplored. Here, we present a first-of-its-kind system that produces novel images of faces by inferring human goals directly from cross-subject brain signals while study subjects are looking at example images. We report on an experiment where brain responses to images of faces were recorded using electroencephalography in 30 subjects, focusing on specific salient visual features (VFs). Preferences toward VFs were decoded from subjects' brain responses and used as implicit feedback for a generative adversarial network (GAN), which generated new images of faces. The results from a follow-up user study evaluating the presence of the target salient VFs show that the images generated from brain feedback represent the goal of the study subjects and are comparable to images generated with manual feedback. The methodology provides a stepping stone toward humans-in-the-loop image generation.

Perceptual metric for face image quality with pixel-level interpretability

This paper tackles the shortcomings of image evaluation metrics in evaluating facial image quality. Conventional metrics do neither accurately reflect the unique attributes of facial images nor correspond with human visual perception. To address these issues, we introduce a novel metric designed specifically for faces, utilizing a learning-based adversarial framework. This framework comprises a generator for simulating face restoration and a discriminator for quality evaluation. Drawing inspiration from facial neuroscience studies, our metric emphasizes the importance of primary facial features, acknowledging that minor changes in the eyes, nose, and mouth can significantly impact perception. Another key limitation of existing image evaluation metrics is their focus on numerical values at the image level, without providing insight into how different areas of the image contribute to the overall assessment. Our proposed metric offers interpretability regarding how each region of the image is evaluated. Comprehensive experimental results confirm that our face-specific metric surpasses traditional general image quality assessment metrics for facial images, including both full-reference and no-reference methods. The code and models are available at https://github.com/AIM-SKKU/IFQA.

Talking Face Generation With Audio-Deduced Emotional Landmarks.

The goal of talking face generation is to synthesize a sequence of face images of the specified identity, ensuring the mouth movements are synchronized with the given audio. Recently, image-based talking face generation has emerged as a popular approach. It could generate talking face images synchronized with the audio merely depending on a facial image of arbitrary identity and an audio clip. Despite the accessible input, it forgoes the exploitation of the audio emotion, inducing the generated faces to suffer from emotion unsynchronization, mouth inaccuracy, and image quality deficiency. In this article, we build a bistage audio emotion-aware talking face generation (AMIGO) framework, to generate high-quality talking face videos with cross-modally synced emotion. Specifically, we propose a sequence-to-sequence (seq2seq) cross-modal emotional landmark generation network to generate vivid landmarks, whose lip and emotion are both synchronized with input audio. Meantime, we utilize a coordinated visual emotion representation to improve the extraction of the audio one. In stage two, a feature-adaptive visual translation network is designed to translate the synthesized landmarks into facial images. Concretely, we proposed a feature-adaptive transformation module to fuse the high-level representations of landmarks and images, resulting in significant improvement in image quality. We perform extensive experiments on the multi-view emotional audio-visual dataset (MEAD) and crowd-sourced emotional multimodal actors dataset (CREMA-D) benchmark datasets, demonstrating that our model outperforms state-of-the-art benchmarks.

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Facial expression recognition (FER) plays a pivotal role in various applications, ranging from human-computer interaction to psychoanalysis. To improve the accuracy of facial emotion recognition (FER) models, this study focuses on enhancing and augmenting FER datasets. It comprehensively analyzes the Facial Emotion Recognition dataset (FER13) to identify defects and correct misclassifications. The FER13 dataset represents a crucial resource for researchers developing Deep Learning (DL) models aimed at recognizing emotions based on facial features. Subsequently, this article develops a new facial dataset by expanding upon the original FER13 dataset. Similar to the FER + dataset, the expanded dataset incorporates a wider range of emotions while maintaining data accuracy. To further improve the dataset, it will be integrated with the extended Cohn-Kanade (CK+) dataset.This paper investigates the application of modern DL models to enhance emotion recognition in human faces. By training a new dataset, the study demonstrates significant performance gains compared with its counterparts. Furthermore, the article examines recent advances in FER technology and identifies critical requirements for DL models to overcome the inherent challenges of this task effectively. The study explores several DL architectures for emotion recognition in facial image datasets, with a particular focus on convolutional neural networks (CNNs). Our findings indicate that complex architecture, such as EfficientNetB7, outperforms other DL architectures, achieving a test accuracy of 78.9 %. Notably, the model surpassed the EfficientNet-XGBoost model, especially when used with the new dataset. Our approach leverages EfficientNetB7 as a backbone to build a model capable of efficiently recognizing emotions from facial images. Our proposed model, EfficientNetB7-CNN, achieved a peak accuracy of 81 % on the test set despite facing challenges such as GPU memory limitations. This demonstrates the model's robustness in handling complex facial expressions. Furthermore, to enhance feature extraction and attention mechanisms, we propose a new hybrid model, CBAM-4CNN, which integrates the convolutional block attention module (CBAM) with a custom 4-layer CNN architecture. The results showed that the CBAM-4CNN model outperformed existing models, achieving higher accuracy, precision, and recall metrics across multiple emotion classes. The results highlight the critical role of comprehensive and diverse data in enhancing model performance for facial emotion recognition.

IMPLEMENTASI ALGORITMA KNN UNTUK DETEKSI GENDER DAN USIA BERBASIS PENGENALAN WAJAH

In the modern era, face recognition is one of the important technologies used in various security and data analysis applications. This paper aims to use Python to implement the K-Nearest Neighbors (KNN) algorithm to detect gender and age through face recognition. The main problem faced is the accuracy in classifying gender and estimating age from various facial images with lighting, pose, and expression. This study uses various steps to collect facial data, use image processing techniques to extract features, and use the KNN algorithm for classification. The results show that applying KNN can detect gender and age from faces with sufficient accuracy, although there are some problems with extreme lighting and pose conditions. These findings indicate that the KNN algorithm can be used in facial recognition applications to detect gender and age. By fixing parameters and improving data quality, further improvements can be achieved.

Feasibility of using deep learning to detect malnutrition phenotypically based on facial images: a cross-sectional study

Feasibility of using deep learning to detect malnutrition phenotypically based on facial images: a cross-sectional study

Class‐wised domain decoupling‐guided adversarial feature learning for cross‐age face recognition

AbstractHow to extract age‐independent identity features from face images has long been the major challenge in cross‐age face recognition task. In this letter, a class‐wised domain decoupling‐guided adversarial feature learning network to extract age‐independent face features is proposed. In this model, an adversarial feature learning module is designed to decompose identity features and age features. Considering that artificially designed age interference suppression strategies are difficult to fit the complex relationship between identity features and age features, an adversarial training strategy incorporated with an attention mechanism in the adversarial feature learning module is introduced to suppress age interference adaptively. Besides, under the idea of maximizing the distribution difference of identity feature domain and age feature domain, a class‐wised domain decoupling module is further designed to guide the model to extract age‐independent identity features. The proposed model has been validated on the well‐known public datasets CALFW and CACD‐VS, where it achieved remarkable recognition accuracy rates of 94.5% and 99.5%, respectively. This represents improvements of 4.2% and 0.1% over the state‐of‐the‐art single‐task‐based models, clearly showcasing the effectiveness of the proposed class‐wised domain decoupling‐guided adversarial feature learning model.

Face Recognition with Siamese Networks

Abstract Face recognition method is a common problem in modern Internet life. In this paper, face recognition will be designed based on Siamese network. The traditional methods have some problems, such as being greatly affected by environmental factors, insufficient processing of individual differences, lack of robustness and adaptability. However, the existing Siamese network based method has better recognition effect. In this paper, a face recognition technology based on Siamese network will be proposed. In this method, the features of two face images are extracted by the same neural network, and then these feature vectors are input into a distance metric model to calculate the similarity or distance between them, which is used to compare the similarity between faces. This method reduces the training parameters of the model, improves the utilization of parameters, and the average accuracy can reach 98%. Contrasted with traditional face recognition techniques, it offers enhanced accuracy and resilience, delivering favourable outcomes in real-world applications.