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Journal articles on the topic 'Facial feature points'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Wang, Mengjie, and Weiyang Chen. "Age prediction based on a small number of facial landmarks and texture features." Technology and Health Care 29 (March 25, 2021): 497–507. http://dx.doi.org/10.3233/thc-218047.

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BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture fe

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Li, Hong-an, Yongxin Zhang, Zhanli Li, and Huilin Li. "A Multiscale Constraints Method Localization of 3D Facial Feature Points." Computational and Mathematical Methods in Medicine 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/178102.

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It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that

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Divekar, A. V., and D. C. Gharpure. "Low-compute facial expression recognition using fiducial feature-sets." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1757-899x/1187/1/012025.

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Abstract Facial Expression Recognition is an exciting area of affective computing. As mobile and embedded devices become increasingly ubiquitous, exploration of low-compute approaches for facial expression recognition is essential. Facial landmark points are fiducial features that are used to localize and represent salient regions of the face, such as eyes, nose and lips. Any facial expression can be expressed as an activation of facial muscles in specific parts of the face, thereby affecting the locations of the facial landmark points in those parts. This relationship can be captured concrete

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Choi, Hyun-Chul, Dominik Sibbing, and Leif Kobbelt. "Nonparametric Facial Feature Localization Using Segment-Based Eigenfeatures." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6730249.

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We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image segments pointing to the expected facial feature positions. Each directional vector is calculated by linear combination of eigendirectional vectors which are obtained by a principal component analysis of

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Liang, Yanqiu. "Intelligent Emotion Evaluation Method of Classroom Teaching Based on Expression Recognition." International Journal of Emerging Technologies in Learning (iJET) 14, no. 04 (2019): 127. http://dx.doi.org/10.3991/ijet.v14i04.10130.

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To solve the problem of emotional loss in teaching and improve the teaching effect, an intelligent teaching method based on facial expression recognition was studied. The traditional active shape model (ASM) was improved to extract facial feature points. Facial expression was identified by using the geometric features of facial features and support vector machine (SVM). In the expression recognition process, facial geometry and SVM methods were used to generate expression classifiers. Results showed that the SVM method based on the geometric characteristics of facial feature points effectively

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SOHAIL, ABU SAYEED MD, and PRABIR BHATTACHARYA. "CLASSIFYING FACIAL EXPRESSIONS USING LEVEL SET METHOD BASED LIP CONTOUR DETECTION AND MULTI-CLASS SUPPORT VECTOR MACHINES." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 835–62. http://dx.doi.org/10.1142/s0218001411008762.

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This paper describes a fully automated computer vision system for detection and classification of the seven basic facial expressions using Multi-Class Support Vector Machine (SVM). Facial expressions are communicated by subtle changes in one or more discrete features such as tightening of the lips, raising the eyebrows, opening and closing of eyes or certain combination of them, which can be identified through monitoring the changes in muscle movements (Action Units), located around the regions of mouth, eyes and eyebrows. For classifying facial expressions, an analytic representation of face

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Wang, Chang Yuan, Mei Juan Qu, Hong Bo Jia, and Hong Zhe Bi. "Facial Feature Detection Algorithm Based on Main Characteristics of Eyes." Applied Mechanics and Materials 303-306 (February 2013): 1402–5. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1402.

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This paper proposed a new facial feature points localization algorithm based on main characteristics of eyes.Use the result of pupil center position to initialize the model of hybrid improved active shape model (ASM) and active appearance model (AAM). The algorithm will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it optimizes measure functions of ASM and AAM to judge the convergence of sea

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Ahdid, Rachid, Es-said Azougaghe, Said Safi, and Bouzid Manaut. "Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches." Journal of Electronic Commerce in Organizations 16, no. 1 (2018): 57–71. http://dx.doi.org/10.4018/jeco.2018010105.

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Geometrical features are widely used to descript human faces. Generally, they are extracted punctually from landmarks, namely facial feature points. The aims are various, such as face recognition, facial expression recognition, face detection. In this article, the authors present two feature extraction methods for two-dimensional face recognition. Their approaches are based on facial feature points detection by compute the Euclidean Distance between all pairs of this points for a first method (ED-FFP) and Geodesic Distance in the second approach (GD-FFP). These measures are employed as inputs

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Duchovičová, Soňa, Barbora Zahradníková, and Peter Schreiber. "Facial Composite System Using Real Facial Features." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 22, no. 35 (2014): 9–15. http://dx.doi.org/10.2478/rput-2014-0029.

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Abstract Facial feature points identification plays an important role in many facial image applications, like face detection, face recognition, facial expression classification, etc. This paper describes the early stages of the research in the field of evolving a facial composite, primarily the main steps of face detection and facial features extraction. Technological issues are identified and possible strategies to solve some of the problems are proposed.

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Li, Shuangyan, Min Ji, Ming Chen, and Lanzhi Chen. "Facial length and angle feature recognition for digital libraries." PLOS ONE 19, no. 7 (2024): e0306250. http://dx.doi.org/10.1371/journal.pone.0306250.

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With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based o

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11

Zhou, Yue, Yin Li, Zheng Wu, and Meilin Ge. "Robust facial feature points extraction in color images." Engineering Applications of Artificial Intelligence 24, no. 1 (2011): 195–200. http://dx.doi.org/10.1016/j.engappai.2010.09.001.

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12

Wang, Qingwei, Xiaolong Zhang, and Xiaofeng Li. "Facial feature point recognition method for human motion image using GNN." Mathematical Biosciences and Engineering 19, no. 4 (2022): 3803–19. http://dx.doi.org/10.3934/mbe.2022175.

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<abstract> <p>To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feat

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Paul, Okuwobi Idowu, and Yong Hua Lu. "Facial Prediction and Recognition Using Wavelets Transform Algorithm and Technique." Applied Mechanics and Materials 666 (October 2014): 251–55. http://dx.doi.org/10.4028/www.scientific.net/amm.666.251.

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An efficient facial representation is a crucial step for successful and effective performance of cognitive tasks such as object recognition, fixation, facial recognition system, etc. This paper demonstrates the use of Gabor wavelets transform for efficient facial representation and recognition. Facial recognition is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Gabor wavelet transform is used for facial features vector construction due to its powerful representation of the behavior of receptive fields in human visu

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Wang, Chang Yuan, Jing Wang, and Mei Juan Qu. "Active Shape and Active Apparent Hybrid Model of Human Facial Feature Points Localization Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 2284–87. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2284.

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An improved active shape model (ASM) and active appearance model (AAM) based new method is proposed, this method will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it uses different measure functions to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

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15

Fu, You Jia, Jian Wei Li, and Ru Xi Xiang. "Enhance ASM Based on DCT-SVM for Facial Feature Points Localization." Applied Mechanics and Materials 121-126 (October 2011): 820–24. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.820.

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Focused on the facial feature points localization, the enhance ASM algorithm based on modeling texture by the DCT-SVM is proposed. First, the statistical shape model is built. Then, some key feature points are selected and their texture models are built by the DCT-SVM. In the subsequent searching, the feature points are divided into two classes based on their reliability gained by DCT-SVM detector, by combining the reliable feature points to the shape constraint, the original shape can finally match to the target face. Experiments show the algorithm is robust to the expressions change and can

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16

Hirayama, Kosuke, Sinan Chen, Sachio Saiki, and Masahide Nakamura. "Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points." Sensors 21, no. 20 (2021): 6726. http://dx.doi.org/10.3390/s21206726.

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To capture scientific evidence in elderly care, a user-defined facial expression sensing service was proposed in our previous study. Since the time-series data of feature values have been growing at a high rate as the measurement time increases, it may be difficult to find points of interest, especially for detecting changes from the elderly facial expression, such as many elderly people can only be shown in a micro facial expression due to facial wrinkles and aging. The purpose of this paper is to implement a method to efficiently find points of interest (PoI) from the facial feature time-ser

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17

Wu, Jie, Qing He, Ran Zhou, Chao Hu, and Q. H. Meng. "A Novel Method of Extracting Facial Feature Points Based on 3D Rotation and ASM." Applied Mechanics and Materials 58-60 (June 2011): 1466–70. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1466.

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Precise extraction of facial feature points is fundamental to a variety of applications including face processing and human-machine interface. In this paper, a novel method of extracting facial feature points for profile faces is presented. This program is mainly based on a 3D rotation model of head and Active Shape Model (ASM). First we transform a profile face to a corresponding frontal face. Then, we implement the ASM program on the frontal face image. According to the relation between the profile face and frontal face, the final position of feature points on the profile face is obtained. W

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18

Wang, Junnan, Rong Xiong, and Jian Chu. "Facial feature points detecting based on Gaussian Mixture Models." Pattern Recognition Letters 53 (February 2015): 62–68. http://dx.doi.org/10.1016/j.patrec.2014.11.004.

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19

Qin, Xi Wen, Hong Yu Zhang, and Xiao Gang Dong. "Facial Feature Location Based on Improved ASM." Advanced Materials Research 546-547 (July 2012): 1398–403. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1398.

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It makes a series of improvements of the traditional oriented way of human face, which is based on ASM. First, we orientate some outstanding feature points on the basis of the results of face test, then make use of these feature points to initialize model. Second, we get some statistics information of the edge in the training process and take a vertical range of the edge in a feature point. According to the scope of positioning, we looked at the local search results on its position before reconstruction the local adjust, thus preventing the search results from departing from the feature pointe

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20

Chopparapu, SaiTeja, and Beatrice Seventline Joseph. "A hybrid facial features extraction-based classification framework for typhlotic people." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 338–49. http://dx.doi.org/10.11591/eei.v13i1.5628.

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Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implement

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Yeh, Jih Pin, Chen Yu Kao, Chung Yung Chen, and Hwei Jen Lin. "A Flexible Facial Feature Replacement System." Advanced Materials Research 403-408 (November 2011): 2958–61. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2958.

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In this study, we propose a facial feature replacement system, which uses the triangulation algorithm to perform facial feature replacement in each segmented triangular region associated with control points. The experimental results show that our system provides quite natural composite images. In addition, the system is flexible and has no limit in the shape, size, and plane rotation of the faces which are processed.

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Li, Bo, Xiao Qin Gu, and Man Huai Lu. "Facial Feature Extraction Algorithm Based on Machine Dynamic Vision." Applied Mechanics and Materials 380-384 (August 2013): 4052–56. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4052.

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In video image sequences, assume that face forms larger interference in athletic process and use traditional algorithm to extract facial features which may lead to target pixel blending and feature missing problems. Three-dimensional face reconstruction has poor authenticity and characteristic distortion. In order to solve this problem, this paper proposes an anti-interference three-dimensional motion face feature extraction method based on multiple target constraint stereoscopic vision algorithm. Extract different facial images target feature points from video sequence, accurately calculate c

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23

Dixit, Anjali, and Tanmay Kasbe. "Multi-feature based automatic facial expression recognition using deep convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1406–19. https://doi.org/10.11591/ijeecs.v25.i3.pp1406-1419.

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Deep multi-task learning is one of the most challenging research topics widely explored in the field of recognition of facial expression. Most deep learning models rely on the class labels details by eliminating the local information of the sample data which deteriorates the performance of the recognition system. This paper proposes multi-feature-based deep convolutional neural networks (D-CNN) that identify the facial expression of the human face. To enhance the accuracy of recognition systems, the multi-feature learning model is employed in this study. The input images are preprocessed and e

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Liu, Qingtong, and Ziyu Xue. "Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery Identification." Security and Communication Networks 2023 (May 15, 2023): 1–10. http://dx.doi.org/10.1155/2023/1648912.

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At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine fa

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Dixit, Anjali, and Tanmay Kasbe. "Multi-feature based automatic facial expression recognition using deep convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1406. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1406-1419.

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<p><span>Deep multi-task learning is one of the most challenging research topics widely explored in the field of recognition of facial expression. Most deep learning models rely on the class labels details by eliminating the local information of the sample data which deteriorates the performance of the recognition system. This paper proposes multi-feature-based deep convolutional neural networks (D-CNN) that identify the facial expression of the human face. To enhance the accuracy of recognition systems, the multi-feature learning model is employed in this study. The input images a

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Salmam, Fatima Zahra, Abdellah Madani, and Mohamed Kissi. "Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (2018): 52. http://dx.doi.org/10.11591/ijece.v8i1.pp52-59.

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The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic

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Fatima, Zahra Salmam, Madani Abdellah, and Kissi Mohamed. "Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (2018): 52–59. https://doi.org/10.11591/ijece.v8i1.pp52-59.

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The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic

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Wang, Xukang, Guanghua Tan, and Chunming Gao. "An Improved Conditional Regression Forests for Facial Feature Points Detection." Information Technology Journal 13, no. 13 (2014): 2159–64. http://dx.doi.org/10.3923/itj.2014.2159.2164.

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Stiefelhagen, Rainer, Jie Yang, and Alex Waibel. "A Model-Based Gaze Tracking System." International Journal on Artificial Intelligence Tools 06, no. 02 (1997): 193–209. http://dx.doi.org/10.1142/s0218213097000116.

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In this paper we present a non-intrusive model-based gaze tracking system. The system estimates the 3-D pose of a user's head by tracking as few as six facial feature points. The system locates a human face using a statistical color model and then finds and tracks the facial features, such as eyes, nostrils and lip corners. A full perspective model is employed to map these feature points onto the 3D pose. Several techniques have been developed to track the features points and recover from failure. We currently achieve a frame rate of 15+ frames per second using an HP 9000 workstation with a fr

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ZHANG, ZHENGYOU. "FEATURE-BASED FACIAL EXPRESSION RECOGNITION: SENSITIVITY ANALYSIS AND EXPERIMENTS WITH A MULTILAYER PERCEPTRON." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (1999): 893–911. http://dx.doi.org/10.1142/s0218001499000495.

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In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducial points on a face, and a set of multiscale and multiorientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furth

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Shindo, Shyota, Takaaki Goto, Tadaaki Kirishima, and Kensei Tsuchida. "An optimization of facial feature point detection program by using several types of convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (2019): 827. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp827-834.

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<p>Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach using<br />Convolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to

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Wang, Kai, Xi Zhao, Wanshun Gao, and Jianhua Zou. "A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion." Symmetry 10, no. 8 (2018): 308. http://dx.doi.org/10.3390/sym10080308.

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Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then,

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Chen, Liang, and Wei Zheng. "Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion." Electronics 12, no. 10 (2023): 2303. http://dx.doi.org/10.3390/electronics12102303.

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Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering unfavorable factors such as facial occlusion and angle changes that have limited single-feature fatigue state detection methods, we developed our model based on the fusion of body postures and facial features for better accuracy. Using facial key poi

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Paika, Vishal, and Er Pankaj Bhambri. "FACE RECOGNITION USING FUZZY INFERENCE SYSTEM." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 3 (2009): 887–97. http://dx.doi.org/10.24297/ijct.v8i3.3399.

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The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, dep

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Zhang, Wenhui, Wentong Wang, Shuang Zhao, and Bin Sun. "Gray-Edge-HOG feature based cascaded learning for facial landmark detection." MATEC Web of Conferences 189 (2018): 10023. http://dx.doi.org/10.1051/matecconf/201818910023.

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Compared with the traditional statistical models, such as the active shape model and the active appearance model, the facial feature point localization method based on deep learning has improved in accuracy and speed, but there still exist some problems. First, when the traditional deep neural network model targets a data set containing different face poses, it only performs the preprocessing through the initialized face alignment, and does not consider the regularity of the distribution of the feature points corresponding to the face pose during feature extraction. Secondly, the traditional d

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Zhou, Xiangling, Zhongmin Gao, Huanji Gong, and Shenglin Li. "DeFFace: Deep Face Recognition Unlocked by Illumination Attributes." Electronics 13, no. 22 (2024): 4566. http://dx.doi.org/10.3390/electronics13224566.

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General face recognition is currently one of the key technologies in the field of computer vision, and it has achieved tremendous success with the support of deep-learning technology. General face recognition models currently exhibit extremely high accuracy on some high-quality face datasets. However, their performance decreases in challenging environments, such as low-light scenes. To enhance the performance of face recognition models in low-light scenarios, we propose a face recognition approach based on feature decoupling and fusion (DeFFace). Our main idea is to extract facial-related feat

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Chen, Long, Guojiang Xin, Yuling Liu, and Junwei Huang. "Driver Fatigue Detection Based on Facial Key Points and LSTM." Security and Communication Networks 2021 (June 12, 2021): 1–9. http://dx.doi.org/10.1155/2021/5383573.

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software l

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Hsieh, Chen Chiung, and Wei Hsu Chen. "A Face Recognition System Based on ASM Facial Components." Applied Mechanics and Materials 58-60 (June 2011): 2314–19. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2314.

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This paper proposed five new types of facial features for face recognition. Ada-boost is used to detect face firstly. False detected faces are removed by dynamic background modeling and skin color detection. Skewed face is also calibrated to achieve higher accuracy. Based on Active Shape Modeling, the five new types of facial features including gradient histograms of facial components, vertical/horizontal projection of facial edge points, signature of facial components, multiple vertical/horizontal line segments within facial shape, and face template could be extracted. According to the classi

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Di, Cheng, Jing Peng, Yihua Di, and Siwei Wu. "3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network." Complexity 2021 (May 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6752120.

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Through the analysis of facial feature extraction technology, this paper designs a lightweight convolutional neural network (LW-CNN). The LW-CNN model adopts a separable convolution structure, which can propose more accurate features with fewer parameters and can extract 3D feature points of a human face. In order to enhance the accuracy of feature extraction, a face detection method based on the inverted triangle structure is used to detect the face frame of the images in the training set before the model extracts the features. Aiming at the problem that the feature extraction algorithm based

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Kim, Hyeon-Woo, Hyung-Joon Kim, Seungmin Rho, and Eenjun Hwang. "Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network." Applied Sciences 10, no. 7 (2020): 2253. http://dx.doi.org/10.3390/app10072253.

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Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a large number of feature points for landmark detection and tracking usually requires excessive processing time. On the contrary, relying on too few feature points cannot accurately represent diverse landmark properties, such as shape. To extract the 68 most popular facial landmark points efficiently, in our previous study, we proposed a mode

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Kai, Wang, Jun An, Xi Zhao, and Jianhua Zou. "Accurate landmarking from 3D facial scans by CNN and cascade regression." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840007. http://dx.doi.org/10.1142/s0219691318400076.

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Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolut

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Ching-Ting Tu and Jenn-Jier James Lien. "Automatic Location of Facial Feature Points and Synthesis of Facial Sketches Using Direct Combined Model." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 4 (2010): 1158–69. http://dx.doi.org/10.1109/tsmcb.2009.2035154.

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Wu, Jian Jun, Jian Jun Yan, Fu Feng Li, Yi Qin Wang, Rui Guo, and Guo Ping Liu. "AAM Based Facial Feature Region Segmentation in Traditional Chinese Medicine Complexion Diagnosis." Advanced Materials Research 790 (September 2013): 535–38. http://dx.doi.org/10.4028/www.scientific.net/amr.790.535.

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Fast and accurate segmentation of the facial feature regions is of great significance to research on objectification of complexion diagnosis. In this paper, we used the active appearance model (AAM) system to accurately locate 68 key points, and segmented the face region simultaneously. According to the Chinese medicine complexion-viscera principle, five face regions representing the five internal organs were segmented by using 68 key points. Experiments have shown that this method was efficient and fast for facial feature region segmentation, and laid a foundation for further research of obje

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Mahajan, Palak, Pawanesh Abrol, and Parveen Kumar Lehana. "Thermal imaging-based identification of facial features in noisy environment." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 3 (2024): 333. http://dx.doi.org/10.11591/ijict.v13i3.pp333-343.

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<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in

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Palak, Mahajan, Abrol Pawanesh, and Kumar Lehana Parveen. "Thermal imaging-based identification of facial features in noisy environment." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 333–43. https://doi.org/10.11591/ijict.v13i3.pp333-343.

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Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction pr

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Liu, Yuanyuan, Jingying Chen, Cunjie Shan, Zhiming Su, and Pei Cai. "A Hierarchical Regression Approach for Unconstrained Face Analysis." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 08 (2015): 1556011. http://dx.doi.org/10.1142/s021800141556011x.

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Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the efficiency and robustness. To eliminate n

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Choirina, Priska, Ulla Delfana Rosiani, Indah Martha Fitriani, and Rijalul Baqi. "Facial Micro Expression Recognition for Feature Point Tracking using Apex Frames on CASME II Database." Sinkron 8, no. 1 (2023): 81–89. http://dx.doi.org/10.33395/sinkron.v8i1.11946.

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Micro-expressions are facial expressions that occur inadvertently to hide true feelings (emotional leaks). Although previous studies used the entire face area and all frames in the video dataset, this resulted in relatively long computation time and data redundancy. The main contribution of this research is to apply recognition micro-expression analysis using a comparison of apex frames with manual (handcrafted) and random sampling of frames and applying feature point tracking to the brow area and corners of the lips. The method for forming feature points in the facial area uses Discriminative

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Zheng, Yinhuan, Beizhan Wang, and Yilong Zheng. "68 Face Feature Points Detection Based on Cascading Convolutional Neural Network with Small Filter." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 135–42. http://dx.doi.org/10.54097/hset.v9i.1731.

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Facial detection has received more and more attention in the past two decades. Due to the pose, occlusion, and illumination changes in the photo, the detection task is quite a challenge in an unconstrained environment. This paper proposed a cascaded convolutional neural network DCNNSF-CFC (Deep Convolution Neural Network with Small Filter-with Coarse-to-Fine Cascade) to localize large facial landmarks to improve the accuracy and robustness of network prediction which based on the original small filter deep convolutional neural network. Each network is trained separately to locally refine a sub

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Xu, Yali, Junli Zhao, Zhihan Lyu, Zhimei Zhang, Jinhua Li, and Zhenkuan Pan. "Automatic facial feature points location based on deep learning: a review." Journal of Image and Graphics 26, no. 11 (2021): 2630–44. http://dx.doi.org/10.11834/jig.200278.

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Li, Ying. "Multi-Template ASM and Its Application in Facial Feature Points Detection." Journal of Computer Research and Development 44, no. 1 (2007): 133. http://dx.doi.org/10.1360/crad20070119.

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