Facial Texture Research Articles

Intelligent voting via face recognition and detection

A tool for facial recognition is a technology that can recognise or verify a person from a digital image or from a source of video. There are many ways facial recognition systems work, but usually they work by matching selected facial features within a database from a given image with faces. It is also known as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analysing patterns based on the individual’s facial textures and shape. In this method, we use three authentication steps for the electors will be used. The first step is the verification of the UID, the second step is the number of the voter card and the third step is the verification, which includes different algorithms for facial recognition. We will offer a comparative analysis between these algorithms in this paper, namely: FisherFace, SURF & Eigenface.

Face Verification across Aging using Deep Learning with Histogram of Oriented Gradients

One of the complex procedures which affect man’s face shape and texture is facial aging. These changes tend to deteriorate the efficacy of systems that automatically verify faces. It seems that CNN (also known as Convolutional Neural Networks) are thought to be one of the most common deep learning approaches where multiple layers are trained robustly while maintaining the minimum number of learned parameters to improve system performance. In this paper, a deeper model of convolutional neural network is fitted with Histogram of Oriented Gradients (HOG) descriptor to handle feature extraction and classification of two face images with the age gap is proposed. Furthermore, the model has been trained and tested in the MORPH and FG-NET datasets. Experiments on FG-NET achieve a state of the arts accuracy (reaching 100%) while results on MORPH dataset have significant improvements in accuracy of 99.85%.

Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition

In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFAR) approach in a hierarchical framework is proposed by coalescing the intensity-based method, pose estimation, and novel dedicated neonatal Face Feature Selection (FFS) algorithm. The intensity-based method is used for face detection, followed by the facial pose estimation algorithm and FFS are dedicated to neonatal pose and face feature recognition, respectively. In this study, video-data of 19 neonates' were collected from the Children's Hospital affiliated to Fudan University, Shanghai, to evaluate the proposed NFAR approach. The results show promising performance to detect the neonatal face, pose estimation (-45°, 45°), and facial features (nose, mouth, and eyes) recognition. The NFAR approach exhibits a sensitivity, accuracy, and specificity of 98.7%, 98.5%, and, 95.7% respectively, for the newborn babies at the frontal (0°) facial region. The neonatal face and its attributes recognition can be expected to detect neonate's medical abnormalities unobtrusively by examining the variation in newborn facial texture pattern.

Guided Cascaded Super-Resolution Network for Face Image

The image super-resolution algorithm can overcome the imaging system’s hardware limitation and obtain higher resolution and clearer images. Existing super-resolution methods based on convolutional neural networks(CNN) can learn the mapping relationship between high-resolution(HR) and low-resolution(LR) images. However, when the reconstruction target is a face image, the reconstruction results often have problems that the face area is too smooth and lacks details. We propose a guided cascaded face super-resolution network, called guided cascaded super-resolution network (GCFSRnet). GCFSRnet takes the LR image and a high-quality guided image as inputs, and it consists of a pose deformation module and a super-resolution network. Firstly, the pose deformation module converts the guide image’s posture into the same as the low-resolution face image based on 3D fitting and 3D morphable model (3DMM). Then, the LR image and the deformed guide image are used as input of the super-resolution network. The super-resolution networks are formed by a cascade of two layers of networks, which extract different features. During the reconstruction process, the guide image can provide real facial details and help generate subtle facial textures. The cascade structure of a super-resolution network can gradually extract features and restore different levels of image details. The experimental results on the CASIA Web Face and CelebA datasets show that the proposed method can generate facial images with clear outlines and rich details, which are superior to other state-of-the-art methods such as SRResNet, SRGAN, VDSR, DBPN, etc.

Flank face texture design to suppress chatter vibration in cutting

Process damping is useful in improving chatter stability in a low cutting speed range. This paper presents a texture design on tool flank faces that can effectively generate process damping. A convex structure on the flank face dampens chatter vibration even at general cutting speeds. An orthogonal cutting simulation utilizing a finite element analysis was conducted to estimate process damping force coefficients that are the functions of cutting and vibration conditions and tool geometry. Sufficient damping effect was predicted using the proposed texture via a chatter stability analysis in frequency domain. Face turning experiments verified the significant chatter suppression effect.

3D texture-based face recognition system using fine-tuned deep residual networks.

As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

Non-invasive Diabetes Detection using Facial Texture Features Captured in a Less Restrictive Environment

The prevalence of Diabetes Mellitus (DM) worldwide has risen dramatically with 1 of 3 deaths happening in Western Pacific region according to the 2017 report of International Diabetes Federation. The Philippines ranks 5th in WP with the most cases of diabetes. Local experts and IDF estimate that half of the people with diabetes are unaware they have it and will likely remain undiagnosed. Conventional ways to detect if a person has diabetes are often invasive and painful such as puncturing fingers for blood sample. Though non-invasive DM detection techniques have gained consideration in more analysts, presently they have restrictive set-up for image capture. This paper explores the performance of using mobile device as a convenient tool for image capture of DM and healthy dataset for non-invasive detection using facial block texture features and Gabor filter. Filipino participants that undergo regular check-ups for diabetes monitoring were chosen within the age inclusion criteria of 20 to 79 years old in which surveys for Philippines assessed the occurrence of diabetes to be most prevalent according to IDF and World Health reports. For each subject, a mobile device 12mp and 7mp cameras were used to take the photo placed 30 cm in front of the face under normal lighting condition to ensure full coverage and avoid unnecessary background. A ratio of 70:30 training to testing set was maintained and extracted facial blocks were classified using SVM and KNN. A total of 100 images from each camera were captured, preprocessed, filtered and iterated to compare performance of data. 90% accuracy, 93% sensitivity and 93% specificity were achieved for 12mp with SVM. For the 7mp camera, an accuracy of 80% using SVM and 93% sensitivity using KNN were achieved after increasing the predictors obtained for classification.

Face Recognition Through Symbolic Modeling of Face Graphs and Texture

Face recognition helps in authentication of the user using remotely acquired facial information. The dynamic nature of face images like pose, illumination, expression, occlusion, aging, etc. degrades the performance of the face recognition system. In this paper, a new face recognition system using facial images with illumination variation, pose variation and partial occlusion is presented. The facial image is described as a collection of three complete connected graphs and these graphs are represented as symbolic objects. The structural characteristics, i.e. graph spectral properties, energy of graph, are extracted and embedded in a symbolic object. The texture features from the cheeks portions are extracted using center symmetric local binary pattern (CS-LBP) descriptor. The global features of the face image, i.e. length and width, are also extracted. Further symbolic data structure is constructed using the above features, namely, the graph spectral properties, energy of graph, global features and texture features. User authentication is performed using a new symbolic similarity metric. The performance is investigated by conducting the experiments with AR face database and VTU-BEC-DB multimodal database. The experimental results demonstrate an identification rate of 95.97% and 97.20% for the two databases.

Texture features extraction based on GLCM for face retrieval system

Texture features play an important role in most image retrieval techniques to obtain results of high accuracy. In this work, the face image retrieval method considering texture analysis and statistical features has been proposed. Textile features can also be extracted using the GLCM tool. In this research, the GLCM calculation method involves two phases, first: some of the previous image processing techniques work together to get the best results to determine the big object of the face image (center of face image) then, the gray level co-occurrence matrix GLCM is computed for gray face image and then some statistical texture features with second-order are extracted. In the second phase, the facial texture features are retrieved by finding the minimum distance between texture features of an unknown face image with the texture features of face images that are stored in the database system. The experimental results show that the proposed method is capable to achieve high accuracy degree in face image retrieval.

Towards High Fidelity Face Frontalization in the Wild

Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.

18∼19세기 조선과 에도의 색조화장 연구

This study analyzed the makeup of Joseon and Edo reflecting the sociocultural aspect and trend of the time as the 18SUPth/SUP and 19SUPth/SUP centuries female culture, and also examined the characteristics in the modern perspective. This study considered the makeup expressed in genre paintings of Joseon and Ukiyo-e of Edo by focusing on the powder makeup, eyebrow makeup, and rouge makeup. The concrete characteristics are as follows. First, powder makeup in the 18SUPth/SUP and 19SUPth/SUP centuries Joseon and Edo was expressed into dry texture with the use of powder and lively texture with the use of cheek rouge. The expression of lively facial texture in the modern Joseon and Edo was also viewed as an aspect of female culture aiming to reveal the individuality and difference in the relationship with eyebrow shape and color expression. Second, he eyebrow makeup of Joseon and Edo women was classified into straight shape, rising shape, slanted shape, arch shape, standard shape and hikimayu. The arch-shape eyebrows showing the feminine and sensuous images, were the envy as the eyebrows of beauty, in relation to the sensuality. The straight-shape eyebrows and rising-shape eyebrows expressed the individual and enterprising makeup images. Third, regarding the color expression of rouge for lip makeup, the Joseon and Edo very boldly used R(v) and R(s) respectively. With the dark eyebrows(Bk), cheek rouge in R(p) & RP(b), and lip rouge(R), the intense and fancy makeup was in popular. The characteristic of Edo’s lip rouge was the fancy color combination in accordance with the use of colors like G(v) and PB(dp). The dynamic and bold color expression for makeup was preferred.

Facial Emotion Identification Based on Local Binary Pattern Feature Detector

Objectives: The emotion detection is one of the important fields in computer human interaction and this study plays significant a role for identification facial expression from the images. To identify the single emotion, need a various variability of human shapes such as pose, color, texture, expression, posture and orientation. In this study, we implement Local Binary Pattern (LBP) based filters for identifying the dynamic face textures. And moreover, this approach also provides extension and simplification. Methods/Statistical Analysis: We used built-in FER2013 datasets, the database consisting seven classes (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy). The dataset is divided into three parts testing, validation and training (15% and 70%). The Convolution neural network is trained with feature Descriptor Local Binary Pattern. Findings: The experimental results have demonstrated that local LBP representations are effective in spatial dynamic feature extraction, as they encode the information of image texture configuration while providing local structure patterns. The advantages of our approach include local processing, robustness to monotonic grayscale changes and simple computation. The results show that, the performance LBP based Convolution Neural Network (CNN) model is better than conventional CNN. This research study further helps in image classification and image processing fields. Application/Improvements: It is recommended that LBP should be used for finding the local regions or pattern from the image. The LBP computation and local processing is quite better with robustness and monotonic changes. Keywords: Convolution Neural Network (CNN), Facial Emotion, Facial Expression, Face detection, Expressions, Local Binary Pattern (LBP)

Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

Since the human face preserves the richest information for recognizing individuals, face recognition has been widely investigated and achieved great success in various applications in the past decades. However, face spoofing attacks (e.g., face video replay attack) remain a threat to modern face recognition systems. Though many effective methods have been proposed for anti-spoofing, we find that the performance of many existing methods is degraded by illuminations. It motivates us to develop illumination-invariant methods for anti-spoofing. In this paper, we propose a two-stream convolutional neural network (TSCNN), which works on two complementary spaces: RGB space (original imaging space) and multi-scale retinex (MSR) space (illumination-invariant space). Specifically, the RGB space contains the detailed facial textures, yet it is sensitive to illumination; MSR is invariant to illumination, yet it contains less detailed facial information. In addition, the MSR images can effectively capture the high-frequency information, which is discriminative for face spoofing detection. Images from two spaces are fed to the TSCNN to learn the discriminative features for anti-spoofing. To effectively fuse the features from two sources (RGB and MSR), we propose an attention-based fusion method, which can effectively capture the complementarity of two features. We evaluate the proposed framework on various databases, i.e., CASIA-FASD, REPLAY-ATTACK, and OULU, and achieve very competitive performance. To further verify the generalization capacity of the proposed strategies, we conduct cross-database experiments, and the results show the great effectiveness of our method.

A novel maximum and minimum response-based Gabor (MMRG) feature extraction method for facial expression recognition

In facial expression recognition applications, the images are corrupted with random noise, and this affects the classification accuracy. This article proposes a maximum and Minimum Response-based Gabor (MMRG) that can encode the facial texture more discriminatively and eliminate random noise. Two code images are produced from the available Gabor images. Then, after dividing the code images into grids, feature vectors are formed using histograms. A technique based on the bat algorithm is proposed for the optimization of the Gabor filter banks as Bat Algorithm-based Gabor Optimization (BAGO). The MMRG increases the efficiency of Gabor filter-based features by precisely distinguishing the texture frequencies. It also helps in reducing the dimensions of feature vector which is a major problem in Gabor filter-based feature extraction. Radial Basis Function-Extreme Learning Machine (RBF-ELM) classifier is used for a faster and accurate multi-classification. The proposed approach has been evaluated with six datasets namely, Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK+), Multi-media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and Man–Machine Interaction (MMI) datasets to meet a classification accuracy of 97.2, 97.4, 95.4, 35.4, 87.4 and 82.3% for seven class emotion detection, which is high when compared to other state-of –the-art methods.

A Multi-View Texture Fusion Approach for High Quality 3D Face Modelling

This paper presents an accurate means for the 3D face texture mapping based on a multiple-view 3D scanning system. Given a reconstructed 3D mesh model and a set of calibrated images, a high-quality texture mosaic of the surface can be created by the proposed method. The proposed method aims to avoid noticeable seams, color discontinuity and ghosting problems. The method performs in two steps, i.e. occlusion areas filtering and the fusion of different texture images. The filtering is performed to identity invisible areas from the camera viewpoint and leaves them not textured. To perform the occluded areas filtering, we combine the geometrical features of the objects and the visual range of the camera. Experimental results showed that, texture mapping quality and precision can be improved in comparison with conventional means.

Multi-scale multi-block covariance descriptor with feature selection

This paper investigates a compact face texture representation able to cover the most discriminant features of facial images. The compactness is achieved by the proposed Pyramid Multi-Level (PML) covariance texture descriptor and the feature selection process that is applied on the raw extracted features. In fact, we introduce a framework based mainly on two new aspects. Firstly, we consider an extension of the original covariance descriptor that relies on de-noised covariance matrices obtained using texture descriptors such as local binary pattern and quaternionic local ranking binary pattern images. Secondly, we exploit the resulting covariance descriptor using a PML face representation which allows a multi-level multi-scale feature extraction. Experiments conducted on four public face datasets show the efficacy of the proposed face descriptor and the associated selection schemes.

Patch-based facial texture super-resolution by fitting 3D face models

Incorporating 3D models for face hallucination (FH) is an ill-posed problem in light of the low-resolution (LR) conditions. To deal with the challenges, a specific 3D shape modeling approach targeting LR face images is first proposed. Based on a few automatically detected 2D facial feature points, an adaptive fitting scheme to relax the fixed correspondence assumption on the facial contour is devised, allowing for pose-invariant shape recovery. In order to exploit the obtained 3D shape and pose for FH, a resolution-aware approach for registering the training 3D faces with the LR input is designed to avoid warping the LR face. Finally, the LR image formation process is reformulated to facilitate super-resolution (SR) of the 3D facial texture. Using this interpretation, the Lucas–Kanade algorithm is extended for 3D deformable models to rectify the imperfect landmark-based fitting on LR images in a posterior fashion. In this way, the final patch-wise SR stage is able to produce realistic facial textures and synthesize self-occluded regions for non-frontal poses. All in all, our main contribution is a novel and pragmatic “2D landmarks $$\rightarrow $$ 3D dense shape $$\rightarrow $$ LR fitting refinement $$\rightarrow $$ 3D FH” pipeline dedicated for the LR scenario. To justify its advantages, extensive evaluation is conducted on several publicly available datasets, revealing superior accuracy over state-of-the-art methods in 3D fitting and high-quality SR results for in-the-wild faces with an interocular distance of as few as five pixels. Moreover, the frontalized high-resolution texture is also verified to help boost the performance of cross-pose face recognition.

Face Recognition as a Biometric Application

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

Face Recognition as a Biometric Application

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

Face Frontalization Using an Appearance-Flow-Based Convolutional Neural Network.

Facial pose variation is one of the major factors making face recognition (FR) a challenging task. One popular solution is to convert non-frontal faces to frontal ones on which FR is performed. Rotating faces causes facial pixel value changes. Therefore, existing CNN-based methods learn to synthesize frontal faces in color space. However, this learning problem in a color space is highly non-linear, causing the synthetic frontal faces to lose fine facial textures. In this paper, we take the view that the nonfrontal-frontal pixel changes are essentially caused by geometric transformations (rotation, translation, and so on) in space. Therefore, we aim to learn the nonfrontal-frontal facial conversion in the spatial domain rather than the color domain to ease the learning task. To this end, we propose an appearance-flow-based face frontalization convolutional neural network (A3F-CNN). Specifically, A3F-CNN learns to establish the dense correspondence between the non-frontal and frontal faces. Once the correspondence is built, frontal faces are synthesized by explicitly "moving" pixels from the non-frontal one. In this way, the synthetic frontal faces can preserve fine facial textures. To improve the convergence of training, an appearance-flow-guided learning strategy is proposed. In addition, generative adversarial network loss is applied to achieve a more photorealistic face, and a face mirroring method is introduced to handle the self-occlusion problem. Extensive experiments are conducted on face synthesis and pose invariant FR. Results show that our method can synthesize more photorealistic faces than the existing methods in both the controlled and uncontrolled lighting environments. Moreover, we achieve a very competitive FR performance on the Multi-PIE, LFW and IJB-A databases.