Relevant bibliographies by topics / Facial features expression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Facial features expression'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Facial features expression"

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Moe, Moe Htay. "Feature extraction and classification methods of facial expression: a survey." Computer Science and Information Technologies 2, no. 1 (2020): 26–32. https://doi.org/10.11591/csit.v2i1.p26-32.

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Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial expression recognition system (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recogn
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Yaermaimaiti, Yilihamu, Tusongjiang Kari, and Guohang Zhuang. "Research on facial expression recognition based on an improved fusion algorithm." Nonlinear Engineering 11, no. 1 (2022): 112–22. http://dx.doi.org/10.1515/nleng-2022-0015.

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Abstract This article puts forward a facial expression recognition (FER) algorithm based on multi-feature fusion and convolutional neural network (CNN) to solve the problem that FER is susceptible to interference factors such as non-uniform illumination, thereby reducing the recognition rate of facial expressions. It starts by extracting the multi-layer representation information (asymmetric region local binary pattern [AR-LBP]) of facial expression images and cascading them to minimize the loss of facial expression texture information. In addition, an improved algorithm called divided local d
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Chen, Xiang Zhang, Zhi Hao Yin, Ze Su Cai, and Ding Ding Zhu. "Facial Expression Recognition of Home Service Robots." Applied Mechanics and Materials 411-414 (September 2013): 1795–800. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1795.

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It is of great significance that a home service robot can recognize facial expressions of a human being. This thesis suggests that features of facial expressions be extracted with PCA, and facial expressions be recognized by distance-based Hashing K-nearest neighbor classification. First, Haar-like feature and AdaBoost algorithm is adopted to detect a face and preprocess the face image; then PCA is applied to extract features of the facial expression, those features will be inserted into the hash table; finally, the facial expression can be recognized by K-nearest neighbor classification algor
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Zulhijah Awang Jesemi, Dayang Nur, Hamimah Ujir, Irwandi Hipiny, and Sarah Flora Samson Juan. "The analysis of facial feature deformation using optical flow algorithm." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (2019): 769. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp769-777.

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<span>Facial features deformed according to the intended facial expression. Specific facial features are associated with specific facial expression, i.e. happy means the deformation of mouth. This paper presents the study of facial feature deformation for each facial expression by using an optical flow algorithm and segmented into three different regions of interest. The deformation of facial features shows the relation between facial the and facial expression. Based on the experiments, the deformations of eye and mouth are significant in all expressions except happy. For happy expressio
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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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Qayyum, Huma, Muhammad Majid, Syed Muhammad Anwar, and Bilal Khan. "Facial Expression Recognition Using Stationary Wavelet Transform Features." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9854050.

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Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spat
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Wang, Qing Wei, and Zi Lu Ying. "Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 511-512 (February 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.433.

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This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and
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Yang, Ruilin, and Limin Yan. "P‐5.3: Three‐dimensional Continuous Expression Synthesis Method Based on Facial Expression Feature Map." SID Symposium Digest of Technical Papers 55, S1 (2024): 898–901. http://dx.doi.org/10.1002/sdtp.17231.

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With the development of virtual reality technology, three‐dimensional (3D) images are widely used in various fields. However, the facial features extracted by most facial expression generation methods are not deeply explored. We propose a method for synthesizing 3D continuous facial expressions based on human expression feature maps, which includes an identity converter based on expression feature maps, a facial image generator, and linear interpolation of continuous expressions. Through comparative experiments, this method has shown good stability and fast speed in synthesizing continuous exp
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Wang, Yan, Jianchun Wang, Yanju Li, Ming Yu, Yancong Zhou, and Bo Zhang. "Facial expression recognition with fused handcraft features based on pixel difference local directional number pattern." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 113–23. http://dx.doi.org/10.3233/jifs-200713.

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Facial expression recognition (FER) has been an active research area in recent years, which plays a vital role in national security and human-computer interaction. Due to the lacking of sufficient expression features and facial images, it is challenging to automatically recognize facial expression with high accuracy. In this paper, we propose a fusion handcraft feature method to improve FER from images. Firstly, a new texture feature extraction method PD-LDN (Pixel Difference Local Directional Number pattern) is proposed, which can extract more local information, reduce noise disturbance and f
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Zhu, Xiaoliang, Shihao Ye, Liang Zhao, and Zhicheng Dai. "Hybrid Attention Cascade Network for Facial Expression Recognition." Sensors 21, no. 6 (2021): 2003. http://dx.doi.org/10.3390/s21062003.

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As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) i
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Dissertations / Theses on the topic "Facial features expression"

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Choudhury, Tanzeem Khalid 1975. "FaceFacts : study of facial features for understanding expression." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/61109.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.<br>Includes bibliographical references (p. 79-83).<br>by Tanzeem Khalid Choudhury.<br>S.M.
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de, la Cruz Nathan. "Autonomous facial expression recognition using the facial action coding system." University of the Western Cape, 2016. http://hdl.handle.net/11394/5121.

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>Magister Scientiae - MSc<br>The South African Sign Language research group at the University of the Western Cape is in the process of creating a fully-edged machine translation system to automatically translate between South African Sign Language and English. A major component of the system is the ability to accurately recognise facial expressions, which are used to convey emphasis, tone and mood within South African Sign Language sentences. Traditionally, facial expression recognition research has taken one of two paths: either recognising whole facial expressions of which there are six i.e.
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Moore, Stephen. "The effects of features and pose on facial expression recognition." Thesis, University of Surrey, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540969.

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Ersotelos, Nikolaos. "Highly automated method for facial expression synthesis." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4524.

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The synthesis of realistic facial expressions has been an unexplored area for computer graphics scientists. Over the last three decades, several different construction methods have been formulated in order to obtain natural graphic results. Despite these advancements, though, current techniques still require costly resources, heavy user intervention and specific training and outcomes are still not completely realistic. This thesis, therefore, aims to achieve an automated synthesis that will produce realistic facial expressions at a low cost. This thesis, proposes a highly automated approach fo
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Mushfieldt, Diego. "Robust facial expression recognition in the presence of rotation and partial occlusion." Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3367.

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>Magister Scientiae - MSc<br>This research proposes an approach to recognizing facial expressions in the presence of rotations and partial occlusions of the face. The research is in the context of automatic machine translation of South African Sign Language (SASL) to English. The proposed method is able to accurately recognize frontal facial images at an average accuracy of 75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60◦. It was also shown that the method is able to continue to recognize facial expressions even in the presence of full occlusions of the e
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Bezerra, Giuliana Silva. "A framework for investigating the use of face features to identify spontaneous emotions." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br/handle/123456789/19595.

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Přinosil, Jiří. "Analýza emocionálních stavů na základě obrazových předloh." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-233488.

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This dissertation thesis deals with the automatic system for basic emotional facial expressions recognition from static images. Generally the system is divided into the three independent parts, which are linked together in some way. The first part deals with automatic face detection from color images. In this part they were proposed the face detector based on skin color and the methods for eyes and lips position localization from detected faces using color maps. A part of this is modified Viola-Jones face detector, which was even experimentally used for eyes detection. The both face detectors
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Yu, Kaimin. "Towards Realistic Facial Expression Recognition." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9459.

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Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework
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Khan, Masood Mehmood. "Cluster-analytic classification of facial expressions using infrared measurements of facial thermal features." Thesis, University of Huddersfield, 2008. http://eprints.hud.ac.uk/id/eprint/732/.

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In previous research, scientists were able to use transient facial thermal features extracted from Thermal Infra-Red Images (TIRIs) for making binary distinction between the affective states. For example, thermal asymmetries localised in facial TIRIs have been used to distinguish anxiety and deceit. Since affective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states, computers’ able to make such binary distinctions would not suffice a robust human-computer interaction. This work, for the first time, uses affective-state-spe
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Mistry, Kamlesh. "Intelligent facial expression recognition with unsupervised facial point detection and evolutionary feature optimization." Thesis, Northumbria University, 2016. http://nrl.northumbria.ac.uk/36011/.

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Facial expression is one of the effective channels to convey emotions and feelings. Many shape-based, appearance-based or hybrid methods for automatic facial expression recognition have been proposed. However, it is still a challenging task to identify emotions from facial images with scaling differences, pose variations, and occlusions. In addition, it is also difficult to identify significant discriminating facial features that could represent the characteristic of each expression because of the subtlety and variability of facial expressions. In order to deal with the above challenges, this
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Books on the topic "Facial features expression"

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Darris, Dobbs, ed. Animating facial features and expression. Charles River Media, 1999.

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Dobbs, Darris, and Bill Fleming. Animating Facial Features & Expressions. Charles River Media, 1998.

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Diogo, Rui, and Sharlene E. Santana. Evolution of Facial Musculature. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0008.

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We review the origin and evolution of the facial musculature of mammals and pay special attention to the complex relationships between facial musculature, color patterns, mobility, and social group size during the evolution of humans and other primates. In addition, we discuss the modularity of the human head and the assymetrical use of facial expressions, as well as the evolvability of the muscles of facial expression, based on recent developmental and comparative studies and the use of a powerful new quantitative tool: anatomical networks analysis. We emphasizes the remarkable diversity of p
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Ravensky, N. How to read a person. Facial features, gestures, posture, facial expressions. Book on Demand Ltd., 2018.

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Animating Facial Features & Expressions, Second Edition (Graphics Series). 2nd ed. Charles River Media, 2006.

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Aminoff, Michael J. Sir Charles Bell. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190614966.001.0001.

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Charles Bell (1774–1842) was a Scottish anatomist–surgeon whose original ideas on the nervous system have been equated with those of William Harvey on the circulation. He suggested that the anterior and posterior nerve roots have different functions, and based on their connectivity he showed that different parts of the brain have different functions. He noted that individual peripheral nerves actually contain nerve fibers with different functions, that nerves conduct only in one direction, that sense organs are specialized to receive only one form of sensory stimulus, and that there is a sixth
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Herzog, Lise. Drawing Faces: Learn How to Draw Facial Expressions, Detailed Features, and Lifelike Portraits. Ulysses Press, 2022.

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Drawing Faces: Learn How to Draw Facial Expressions, Detailed Features, and Lifelike Portraits. Ulysses Press, 2022.

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Stojnić, Una. Discourse, Context, and Coherence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791492.003.0006.

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On the received view, the resolution of context-sensitivity is at least partly determined by non-linguistic features of utterance situation. If I say ‘He’s happy’, what ‘he’ picks out is underspecified by its linguistic meaning, and is only fixed through extra-linguistic supplementation: the speaker’s intention, and/or some objective, non-linguistic feature of the utterance situation. This underspecification is exhibited by most context-sensitive expressions, with the exception of pure indexicals, like ‘I.’ While this received view is prima facie appealing, I argue it is deeply mistaken. I def
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Grant, Robert. Neurocutaneous syndromes. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198569381.003.0235.

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This chapter describes several neurocutaneous syndromes, including tuberous sclerosis, neurofibromatosis, Sturge–Weber syndrome, Von-Hippel–Lindau disease and ataxia telangiectasia amongst others.Tuberous sclerosis, also known as Epiloia or Bournville’s Disease, is an autosomal dominant multisystem disease it usually presents in childhood with a characteristic facial rash, adenoma sebaceum, seizures, and sometimes learning difficulties. Central nervous system lesions in tuberous sclerosis are due to a developmental disorder of neurogenesis and neuronal migration. Other organs such as the heart
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Book chapters on the topic "Facial features expression"

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Ptucha, Raymond, and Andreas Savakis. "Facial Expression Recognition Using Facial Features and Manifold Learning." In Advances in Visual Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17277-9_31.

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Akakın, Hatice Çınar, and Bülent Sankur. "Spatiotemporal Features for Effective Facial Expression Recognition." In Trends and Topics in Computer Vision. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35749-7_16.

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Banerjee, Tanmoy, Sayantan De, Sampriti Das, Susmit Sarkar, and Spandan Swarnakar. "Automatic Facial Expression Recognition Using Geometrical Features." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7403-6_45.

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Liu, Zhu, Tingsong Ma, Yuanlun Xie, Hengxin Zhang, Jian Wang, and Wenhong Tian. "Detecting Anomaly Features in Facial Expression Recognition." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92307-5_47.

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Zhang, Meng, Dah-Jye Lee, Alok Desai, Kirt D. Lillywhite, and Beau J. Tippetts. "Automatic Facial Expression Recognition Using Evolution-Constructed Features." In Advances in Visual Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14364-4_27.

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Kyperountas, Marios, and Ioannis Pitas. "Facial Expression Recognition Using Two-Class Discriminant Features." In Biometric ID Management and Multimodal Communication. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04391-8_12.

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Liu, Fang, Zhi-liang Wang, Li Wang, and Xiu-yan Meng. "Facial Expression Recognition Using HLAC Features and WPCA." In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548_12.

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Mourão, André, Pedro Borges, Nuno Correia, and João Magalhães. "Facial Expression Recognition by Sparse Reconstruction with Robust Features." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_13.

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Ronsin, Joseph, Kidiyo Kpalma, and Hua Lu. "Reducing LBP Features for Facial Identification and Expression Recognition." In Advances in Smart Technologies Applications and Case Studies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53187-4_12.

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Kahou, Samira Ebrahimi, Pierre Froumenty, and Christopher Pal. "Facial Expression Analysis Based on High Dimensional Binary Features." In Computer Vision - ECCV 2014 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16181-5_10.

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Conference papers on the topic "Facial features expression"

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Gacav, Caner, Burak Benligiray, Kemal Ozkan, and Cihan Topal. "Facial expression recognition with FHOG features." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404811.

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Durmusoglu, Alptekin, and Yavuz Kahraman. "Facial expression recognition using geometric features." In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2016. http://dx.doi.org/10.1109/iwssip.2016.7502700.

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Ewees, Ahmed A., Hend A. ElLaban, and Rania M. ElEraky. "Features Selection for Facial Expression Recognition." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944459.

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Hachisuka, Satori, Teiyuu Kimura, Kenji Ishida, Hiroto Nakatani, and Noriyuki Ozaki. "Drowsiness Detection Using Facial Expression Features." In SAE 2010 World Congress & Exhibition. SAE International, 2010. http://dx.doi.org/10.4271/2010-01-0466.

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Soyel, Hamit, and Hasan Demirel. "3D facial expression recognition with geometrically localized facial features." In 2008 23rd International Symposium on Computer and Information Sciences. IEEE, 2008. http://dx.doi.org/10.1109/iscis.2008.4717898.

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Tang, Fangqi, and Benzai Deng. "Facial Expression Recognition using AAM and Local Facial Features." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.373.

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Soyel, Hamit, and Hasan Demirel. "Optimal feature selection for 3D facial expression recognition with geometrically localized facial features." In 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control. IEEE, 2009. http://dx.doi.org/10.1109/icsccw.2009.5379476.

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Zhou, Shu-ren, Xi-ming Liang, and Can Zhu. "Support Vector Clustering of Facial Expression Features." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.26.

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Tarannum, Tasnim, Anwesha Paul, and Kamrul Hasan Talukder. "Human expression recognition based on facial features." In 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016. http://dx.doi.org/10.1109/iciev.2016.7760147.

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Cam, Baris Can, Meltem Yalcin, and Hasan Serhan Yavuz. "Facial expression recognition based on locational features." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7496023.

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