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摘要: Heart rate is an important vital characteristic which indicates physical and mental health status. Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly. Therefore, the study of non-contact heart rate measurement methods is of great importance. Based on the principles of photoelectric volumetric tracing, we use a computer device and camera to capture facial images, accurately detect face regions, and to detect multiple facial images 关键词: Face recognition; face analysis; heart rate detection; IPPG signal
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摘要: Automated formulation of sketches from face photos has seen successive growth since the work of Wang and Tang in recent years. Each new methodology is, however, able to partially achieve its objective of sketch synthesis while using pairs of photos and viewed sketches as a training medium. The viewed sketches are also used as a testing medium to determine the success of those methodologies. Resulting sketches do not fully capture all features of the training photos and viewed sketches. Their sim关键词: Face recognition;Fast-RSLCR technique;FCN technique;Image fusion;NLDA;Photo Sketch synthesis
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摘要: One of the main objectives of smart homes is to facilitate daily life by increasing user comfort, with the potential to play a key role in revolutionizing healthcare for the elderly, the disabled and people with functional limitations. To achieve this end, smart homes will have to be able to distinguish the identity of users, their location and the activities they are performing, while also being implemented in a non-invasive way that protects the privacy of these users. Computer vision is one o关键词: Generative Adversarial Networks (GANs);Image translation;Thermal image;Face recognition;Privacy
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摘要: This paper presents a novel approach for Human Face Recognition, namely Regularized Bi-partitioned Entropy Component Analysis (RBECA). This conservative approach regularizes the kernel entropy components by deterring the noise and affecting the lower entropy regions area, making the method robust to noise. The kernel feature space, formed by the kernel entropy component analysis (KECA), is divided into two partitions: the High Entropy Space (HES) and the Low Entropy Space (LES). The noise-laden 关键词: Face recognition;Regularized entropy space;Golden search minimization;Noise stabilization;Kernel feature space;Multi-scale CNN
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摘要: A new joint diagonalization algorithm for a pair of Hermitian quaternion matrices is derived incorporating real structure-preserving strategy. The structure-preserving joint diagonalization algorithm leads to a novel two-dimensional quaternion linear discriminant analysis (2D-QLDA) method for color face recognition and image reconstruction. 2D-QLDA is mathematically characterized by Hermitian quaternion generalized eigenvalue problem. A weighted norm is obtained as a new measurement to determine关键词: Hermitian quaternion matrix;Structure-preserving algorithm;Joint diagonalization;Face recognition;Linear discriminant analysis
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Journal
摘要: Child-face aging and rejuvenation have amassed considerable active research interest, owing to their immense impact on a broad range of social and security applications, e.g., digital entertainment, fashion and wellness, and searching for long-lost children using childhood photos. All current face aging approaches based on generative adversarial networks (GANs) focus on adult images or long-term aging. We present a new large-scale longitudinal Indian child (ICD) benchmark dataset to facilitate f关键词: Child face aging and rejuvenation;Child datasets;Face recognition;Age estimation;Gender preservation;Child trafficking
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Journal
摘要: The loss function, also known as cost function, is used for training a neural network or other machine learning models. Over the past decade, researchers have designed many loss functions for machine learning, such as mean squared error and mean absolute error. However, in deep learning, neurons of the last layer are usually activated by a sigmoid or softmax function. Thus, training with traditional losses would cause lower efficiency and accuracy. Recently, designing loss functions for deep lea关键词: Loss function;Deep learning;Object detection;Face recognition;Image segmentation
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Journal
摘要: The principal component analysis network (PCANet) with predefined filters has recognized as a lightweight convolutional neural networks (CNN) baseline. However, there are three shortages in PCANet: (i) single-scale convolution leads to insufficient feature learning, (ii) the spatial layout information and relationship between channel features are neglected, (iii) lack of nonlinearity because there is no activation function. Therefore, we devise a PCANet alternative dubbed Multi-Scale Spatial pyr关键词: Face recognition;PCA filter;Lightweight CNN;Second-order pooling;Feature learning
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Journal
摘要: Background and objective(#br)Face images often change in posture, Angle, expression, makeup, aging, lighting, and other aspects. The performance of many models with high accuracy in restricted scenes decreases sharply in unrestricted scenes. Therefore, the study of face recognition in unrestricted scenes is of great significance. In this paper, we proposed a face recognition algorithm based on depth map transfer learning to effectively recognize face images taken in unrestricted environment.(#br关键词: Unrestricted environments;Face recognition;Depth map;Transfer learning
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Journal
摘要: Facial recognition is a category of biometric security, used widely in various industries where we identify and authenticate an individuals identity using their face. In the modern deep learning era, face recognition datasets are playing a significant role in achieving state-of-the-art accuracy by acquiring and training millions of face images. Annotating such a large-scale face recognition dataset is challenging due to low-quality face images, and incorrect annotations unknowingly made by annot关键词: Uncertainty learning;Ranking loss;Relabeling mechanism;Face recognition,
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Journal
摘要: Biometric based systems are involved in many areas, from surveillance to user authentication, from autonomous systems to human-robot interactions. Head pose estimation (HPE) is the task to support biometric systems in which any of the biometric traits of the head is involved, as face, ear or iris. This particular biometric branch finds its application in driver attention detection, surveillance for recognition, face frontalization, best frame selection and so on. The goal of HPE is to determine 关键词: Biometrics;Head pose estimation;Face recognition;Frontalization
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Journal
Lafuente-Arroyo Sergio;Martín-Martín Pilar;Iglesias-Iglesias Cristian;Maldonado-Bascón Saturnino;Acevedo-Rodríguez Francisco Javier;
Expert Systems With ApplicationsVolume 197, Issue , 2022, PP
摘要: Most injuries in the elderly are due to falls. The response time, to attend to the critically injured in such fall cases, is crucial to their survival. This paper presents a low-cost, autonomous assistive patrol robot which additionally includes a fallen person detection module with facial recognition that allows identification of patients. Patrol robots could be beneficial for care centers, where there is a considerable number of patients that require care. In these conditions, falls can be gen关键词: Assistive robot;Fallen person detection;Face recognition;Object detector;Convolutional neural network;Support vector machine
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Journal
摘要: These days, face recognition systems are widely being employed in various daily applications such as smart phone unlocking, tracking school attendance, and secure online bank transactions, smarter border control, to name a few. In spite of the remarkable progress, face recognition systems still suffer from occlusions, light variations, camera types and their resolutions. Face recognition is still a dynamic research field. In this paper, we propose an efficient face recognition system based on Ga关键词: Sparse AutoEncoder;Gabor filter bank;Face recognition;PCA+LDA
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Journal
摘要: Deep neural networks (DNN) models have been widely applied in many tasks. However, recent researches have shown that DNN models are vulnerable to backdoor attacks. A number of backdoor attacks on DNN models have been proposed, but almost all the existing backdoor attacks are digital backdoor attacks. However, when launching backdoor attacks in the real physical world, the attack performance will be severely degraded due to a variety of physical constraints. In this paper, we propose a robust phy关键词: Artificial intelligence securitys;Physical backdoor attack;Deep neural networks;Physical transformations;Face recognition
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Journal
摘要: In the current cloud computing era, outsourcing overloaded computations to cloud servers has become an increasingly popular computing paradigm. Meanwhile, face recognition (FR), as a typical and extensively deployed biometric authentication technique in the real world, always involves time-consuming large-scale matrix operations or complex optimization problems. Therefore, it is a naturally actual demand to study the cloud/edge server-assisted FR algorithm. Nevertheless, the sensitivity of the F关键词: Computation outsourcing;Face recognition;Privacy-preserving;Sparse representation classification;ℓ;1;-Minimization
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Journal
摘要: It is still an important and challenging problem for face recognition with occlusion, small sample size, various expressions, and poses, called un-completed face recognition. So we design a simple but effective hybrid-supervision learning frame by fusing the advantages of supervised and unsupervised features. In the supervised branch, we propose an effective feature learning method: HMMFA. In the unsupervised branch, we improve the PCANet to extract more effective local information. In the fusio关键词: Face recognition;Feature fusion;Hybrid supervised learning;Multiple marginal Fisher analysis
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Journal
摘要: As a widely mentioned topic in face recognition, the margin-based loss function enhances the discriminability of face recognition models by applying margin between class decision boundaries. However, there is still room to improve the representation of face features. Local face feature extraction has been employed in traditional face recognition methods, but with the increase of network depth in deep learning, the traditional approach requires a large number of computational resources. In this p关键词: Face recognition;ArcFace;Channel attention;Deep learning
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Journal
摘要: There is an increasing need in eyewitness identification research to identify factors that not only influence identification accuracy but may also impact the confidence-accuracy (CA) relationship. One such variable that has a notable impact on memory for faces is viewing distance, with faces encoded from a shorter distance remembered better than faces encoded at longer differences. In four pre-registered experiments, using both laboratory and online samples, we compared faces viewed at a simulat关键词: Confidence–accuracy relationship;Eyewitness identification;Face recognition;Viewing distance
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Journal
摘要: Adults are experts at recognizing familiar faces across images that incorporate natural within-person variability in appearance (i.e., ambient images). Little is known about children's ability to do so. In the current study, we investigated whether 4- to 7-year-olds (n = 56) could recognize images of their own parent-a person with whom children have had abundant exposure in a variety of different contexts. Children were asked to identify images of their parent that were intermixed with images of关键词: Child development;Face perception;Face recognition;Perceptual development;Personal familiarity;Within-person variability
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Book Chapter
摘要: With the growing concept of smart cities, IoT applications such as face-authenticated smart-home door-lock security systems have gained importance and become popular. The major challenge in implementing the algorithms on IoT devices is a lack of computational power. The major challenge in implementing the algorithms on IoT devices is a lack of computational power. The state-of-the-art methods run successfully in high-end computing units, are typically not used in Raspberry pi, considered as an I关键词: IoT;Door-lock system;HFFCFA;Raspberry pi;Face recognition;Deep learning
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Journal
摘要: Face recognition is widely used and is one of the most challenging tasks in computer vision. In recent years, many face recognition methods based on dictionary learning have been proposed. However, most methods only focus on the resolution of the original image, and the change of resolution may affect the recognition results when dealing with practical problems. Aiming at the above problems, a method of multi-resolution dictionary learning combined with sample reverse representation is proposed 关键词: Face recognition;dictionary learning;multi-resolution;fusion classification;reverse representation
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Journal
AlFawwaz Bader M.;ALShatnawi Atallah;AlSaqqar Faisal;Nusir Mohammad;
DataVolume 7, Issue 6, 2022, PP 80-80
摘要: This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testin关键词: feature fusion;face recognition;Laplacian Pyramid;multi-resolution discrete cosine transform
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Journal
Merolla Stefano;Borella Monica;Santilli Ignazio Michele;Grassi Maria Pia;
NeurocaseVolume , Issue , 2022, PP 1-7
摘要: Prosopamnesia is a face-selective memory disorder in which face learning is impaired, while face-perception disorder (prosopagnosia) and memory disorders for stimuli other than faces are not present. To date, only two cases of prosopamnesia have been reported in adults - one congenital and one secondary to brain damage. This article reports a case of a 68-year-old woman complaining difficulties recognizing persons she had got to know recently. Neuropsychological examination revealed face-specifi关键词: Prosopamnesia;amnesia;face learning;face processing;face recognition;memory;prosopagnosia
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Book Chapter
摘要: The development of artificial intelligence has greatly changed people’s work and lifestyle. Intelligence has appeared in various fields, which has brought great convenience to people. Education is considered to be one of the most important topics for a family. With the family’s emphasis on education, the number of students is increasing year by year, and there is a serious imbalance between the number of teachers and the number of students. Teachers cannot guarantee the quality of education whil关键词: Face;Face recognition;Intelligent education;Management platform
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Journal
Li Yiwen;Zhang Mingming;Liu Shuaicheng;Luo Wenbo;
NeuroImageVolume 258, Issue , 2022, PP 119374-119374
摘要: Humans can detect and recognize faces quickly, but there has been little research on the temporal dynamics of the different dimensional face information that is extracted. The present study aimed to investigate the time course of neural responses to the representation of different dimensional face information, such as age, gender, emotion, and identity. We used support vector machine decoding to obtain representational dissimilarity matrices of event-related potential responses to different face关键词: arousal;decoding;emotion;face recognition;valence
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Journal
摘要: The human face is considered the prime entity in recognizing a person's identity in our society. Henceforth, the importance of face recognition systems is growing higher for many applications. Facial recognition systems are in huge demand, next to fingerprint-based systems. Face-biometric has a highly dominant role in various applications such as border surveillance, forensic investigations, crime detection, access management systems, information security, and many more. Facial recognition syste关键词: Authentication;Biometrics;Computer vision;Deep learning;Face recognition;Image processing;Machine learning
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Journal
摘要: Earlier this year, the European Commission (EC) registered the ‘Civil society initiative for a ban on biometric mass surveillance practices’, a European Citizens’ Initiative. Citizens are thus given the opportunity to authorize the EC to suggest the adoption of legislative instruments to permanently ban biometric mass surveillance practices. This contribution finds the above initiative particularly promising, as part of a new development of bans in the European Union (EU). It analyses the EU’s a关键词: Face recognition;visual data;biometric data;surveillance
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Journal
摘要: The access control methods mainly employ physiological characteristics and the practical relevance to generate sophisticated spoofing attacks led to the development of liveness detection methods to increase the robustness of the identification systems. Among the largely deployed traits such as iris and fingerprint, the face is the commonly used modality in recognition systems due to its non-intrusive nature. The evolving technological progress has made the production of a wide variety of spoofin关键词: Biometrics;Face recognition;Print spoof detection;Volumetric and statistical dispersion measures;GLCM;Supervised learning;Binary classification
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Journal
摘要: This paper presents an optimization of the medication delivery drone with the Internet of Things (IoT)-Guidance Landing System based on direction and intensity of light. The IoT-GLS was incorporated into the system to assist the drone’s operator or autonomous system to select the best landing angles for landing. The landing selection was based on the direction and intensity of the light. The medication delivery drone system was developed using an Arduino Uno microcontroller board, ESP32 DevKitC 关键词: IoT;guidance landing system;light direction;light intensity;drone;face recognition
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Journal
Schwarz Christopher G;Kremers Walter K;Lowe Val J;Savvides Marios;Gunter Jeffrey L;Senjem Matthew L;Vemuri Prashanthi;Kantarci Kejal;Knopman David S;Petersen Ronald C;Jack Clifford R;The Alzheimer's Disease Neuroimaging Initiative;
NeuroImageVolume 258, Issue , 2022, PP 119357-119357
摘要: It is well known that de-identified research brain images from MRI and CT can potentially be re-identified using face recognition; however, this has not been examined for PET images. We generated face reconstruction images of 182 volunteers using amyloid, tau, and FDG PET scans, and we measured how accurately commercial face recognition software (Microsoft Azure's Face API) automatically matched them with the individual participants' face photographs. We then compared this accuracy with the same关键词: Anonymization;De-facing;De-identification;Face Recognition;PET/CT
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