Academic literature on the topic 'Face emotion recognition'
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Journal articles on the topic "Face emotion recognition"
Mallikarjuna, Basetty, M. Sethu Ram, and Supriya Addanke. "An Improved Face-Emotion Recognition to Automatically Generate Human Expression With Emoticons." International Journal of Reliable and Quality E-Healthcare 11, no. 1 (January 1, 2022): 1–18. http://dx.doi.org/10.4018/ijrqeh.314945.
Full textIqbal, Muhammad, Bhakti Yudho Suprapto, Hera Hikmarika, Hermawati Hermawati, and Suci Dwijayanti. "Design of Real-Time Face Recognition and Emotion Recognition on Humanoid Robot Using Deep Learning." Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) 9, no. 2 (October 6, 2022): 149–58. http://dx.doi.org/10.33019/jurnalecotipe.v9i2.3044.
Full textSondawale, Shweta. "Face and Speech Emotion Recognition System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 5621–28. http://dx.doi.org/10.22214/ijraset.2024.61278.
Full textMareeswari V. "Face Emotion Recognition based Recommendation System." ACS Journal for Science and Engineering 2, no. 1 (March 1, 2022): 73–80. http://dx.doi.org/10.34293/acsjse.v2i1.29.
Full textLevitan, Carmel A., Isabelle Rusk, Danielle Jonas-Delson, Hanyun Lou, Lennon Kuzniar, Gray Davidson, and Aleksandra Sherman. "Mask wearing affects emotion perception." i-Perception 13, no. 3 (May 2022): 204166952211073. http://dx.doi.org/10.1177/20416695221107391.
Full textLiao, Songyang, Katsuaki Sakata, and Galina V. Paramei. "Color Affects Recognition of Emoticon Expressions." i-Perception 13, no. 1 (January 2022): 204166952210807. http://dx.doi.org/10.1177/20416695221080778.
Full textWyman, Austin, and Zhiyong Zhang. "API Face Value." Journal of Behavioral Data Science 3, no. 1 (July 13, 2023): 1–11. http://dx.doi.org/10.35566/jbds/v3n1/wyman.
Full textZhang, Zhiqin. "Deep Face Emotion Recognition." Journal of Physics: Conference Series 1087 (September 2018): 062036. http://dx.doi.org/10.1088/1742-6596/1087/6/062036.
Full textLawrence, Louise, and Deborah Abdel Nabi. "The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces." International Journal of Synthetic Emotions 4, no. 2 (July 2013): 34–62. http://dx.doi.org/10.4018/ijse.2013070104.
Full textHomorogan, C., R. Adam, R. Barboianu, Z. Popovici, C. Bredicean, and M. Ienciu. "Emotional Face Recognition in Bipolar Disorder." European Psychiatry 41, S1 (April 2017): S117. http://dx.doi.org/10.1016/j.eurpsy.2017.01.1904.
Full textDissertations / Theses on the topic "Face emotion recognition"
Bate, Sarah. "The role of emotion in face recognition." Thesis, University of Exeter, 2008. http://hdl.handle.net/10036/51993.
Full textTomlinson, Eleanor Katharine. "Face-processing and emotion recognition in schizophrenia." Thesis, University of Birmingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433700.
Full textKuhn, Lisa Katharina. "Emotion recognition in the human face and voice." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11216.
Full textDurrani, Sophia J. "Studies of emotion recognition from multiple communication channels." Thesis, University of St Andrews, 2005. http://hdl.handle.net/10023/13140.
Full textAlashkar, Taleb. "3D dynamic facial sequences analysis for face recognition and emotion detection." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10109/document.
Full textIn this thesis, we have investigated the problems of identity recognition and emotion detection from facial 3D shapes animations (called 4D faces). In particular, we have studied the role of facial (shapes) dynamics in revealing the human identity and their exhibited spontaneous emotion. To this end, we have adopted a comprehensive geometric framework for the purpose of analyzing 3D faces and their dynamics across time. That is, a sequence of 3D faces is first split to an indexed collection of short-term sub-sequences that are represented as matrix (subspace) which define a special matrix manifold called, Grassmann manifold (set of k-dimensional linear subspaces). The geometry of the underlying space is used to effectively compare the 3D sub-sequences, compute statistical summaries (e.g. sample mean, etc.) and quantify densely the divergence between subspaces. Two different representations have been proposed to address the problems of face recognition and emotion detection. They are respectively (1) a dictionary (of subspaces) representation associated to Dictionary Learning and Sparse Coding techniques and (2) a time-parameterized curve (trajectory) representation on the underlying space associated with the Structured-Output SVM classifier for early emotion detection. Experimental evaluations conducted on publicly available BU-4DFE, BU4D-Spontaneous and Cam3D Kinect datasets illustrate the effectiveness of these representations and the algorithmic solutions for identity recognition and emotion detection proposed in this thesis
Bui, Kim-Kim. "Face Processing in Schizophrenia : Deficit in Face Perception or in Recognition of Facial Emotions?" Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3349.
Full textSchizophrenia is a psychiatric disorder characterized by social dysfunction. People with schizophrenia misinterpret social information and it is suggested that this difficulty may result from visual processing deficits. As faces are one of the most important sources of social information it is hypothesized that people suffering from the disorder have impairments in the visual face processing system. It is unclear which mechanism of the face processing system is impaired but two types of deficits are most often proposed: a deficit in face perception in general (i.e., processing of facial features as such) and a deficit in facial emotion processing (i.e., recognition of emotional facial expressions). Due to the contradictory evidence from behavioural, electrophysiological as well as neuroimaging studies offering support for the involvement of one or the other deficit in schizophrenia it is early to make any conclusive statements as to the nature and level of impairment. Further studies are needed for a better understanding of the key mechanism and abnormalities underlying social dysfunction in schizophrenia.
Bellegarde, Lucille Gabrielle Anna. "Perception of emotions in small ruminants." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25915.
Full textChiller-Glaus, Sarah. "Testing the limits of face recognition : identification from photographs in travel documents and dynamic aspects of emotion recognition /." [S.l.] : [s.n.], 2009. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000281129.
Full textMerz, Sabine Psychology Faculty of Science UNSW. "Face emotion recognition in children and adolescents; effects of puberty and callous unemotional traits in a community sample." Publisher:University of New South Wales. Psychology, 2008. http://handle.unsw.edu.au/1959.4/41247.
Full textBloom, Elana. "Recognition, expression, and understanding facial expressions of emotion in adolescents with nonverbal and general learning disabilities." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100323.
Full textAdolescents aged 12 to 15 were screened for LD and NLD using the Wechsler Intelligence Scale for Children---Third Edition (WISC-III; Weschler, 1991) and the Wide Range Achievement Test---Third Edition (WRAT3; Wilkinson, 1993) and subtyped into NVLD and GLD groups based on the WRAT3. The NVLD ( n = 23), matched NLD (n = 23), and a comparable GLD (n = 23) group completed attention, mood, and neuropsychological measures. The adolescent's ability to recognize (Pictures of Facial Affect; Ekman & Friesen, 1976), express, and understand facial expressions of emotion, and their general social functioning was assessed. Results indicated that the GLD group was significantly less accurate at recognizing and understanding facial expressions of emotion compared to the NVLD and NLD groups, who did not differ from each other. No differences emerged between the NVLD, NLD, and GLD groups on the expression or social functioning tasks. The neuropsychological measures did not account for a significant portion of the variance on the emotion tasks. Implications regarding severity of LD are discussed.
Books on the topic "Face emotion recognition"
Dutta, Paramartha, and Asit Barman. Human Emotion Recognition from Face Images. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3883-4.
Full textLambelet, Clément. Happiness is the only true emotion: Clément Lambelet. Paris]: RVB books, 2019.
Find full textA, Tsihrintzis George, ed. Visual affect recognition. Amsterdam: IOS Press, 2010.
Find full textMichela, Balconi, ed. Neuropsychology and cognition of emotional face comprehension, 2006. Trivandrum, India: Research Signpost, 2006.
Find full textN, Emde Robert, Osofsky Joy D, and Butterfield Perry M. 1932-, eds. The IFEEL pictures: A new instrument for interpreting emotions. Madison, Conn: International Universities Press, 1993.
Find full textKonar, Amit, and Aruna Chakraborty. Emotion Recognition. Wiley & Sons, Incorporated, John, 2015.
Find full textDutta, Paramartha, and Asit Barman. Human Emotion Recognition from Face Images. Springer Singapore Pte. Limited, 2021.
Find full textDutta, Paramartha, and Asit Barman. Human Emotion Recognition from Face Images. Springer, 2020.
Find full textAnbarjafari, Gholamreza, Pejman Rasti, Fatemeh Noroozi, Jelena Gorbova, and Rain E. Haamer. Machine Learning for Face, Emotion, and Pain Recognition. SPIE, 2018. http://dx.doi.org/10.1117/3.2322572.
Full textKonar, Amit, and Aruna Chakraborty. Emotion Recognition: A Pattern Analysis Approach. Wiley & Sons, Limited, John, 2015.
Find full textBook chapters on the topic "Face emotion recognition"
Sati, Vishwani, Sergio Márquez Sánchez, Niloufar Shoeibi, Ashish Arora, and Juan M. Corchado. "Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano." In Advances in Intelligent Systems and Computing, 177–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58356-9_18.
Full textAlharbi, Alhasan Ali, Mukta Dhopeshwarkar, and Shubhashree Savant. "Detection of Emotion Intensity Using Face Recognition." In Communications in Computer and Information Science, 207–13. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0507-9_18.
Full textRamsaran, Pallavi, and Leckraj Nagowah. "Music Recommendation Based on Face Emotion Recognition." In Smart Mobile Communication & Artificial Intelligence, 180–91. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56075-0_18.
Full textWallbott, Harald G. "Recognition of Emotion in Specific Populations: Compensation, Deficit or Specific (Dis)Abilities?" In The Human Face, 169–87. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1063-5_9.
Full textLo Presti, Liliana, and Marco La Cascia. "Ensemble of Hankel Matrices for Face Emotion Recognition." In Image Analysis and Processing — ICIAP 2015, 586–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_54.
Full textKala, Vallu Sri Satya, Vanka Bhavyasri, and S. Vigneshwari. "Face Recognition Based Attendance System and Emotion Classification." In Lecture Notes in Electrical Engineering, 625–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7511-2_63.
Full textKhandelwal, Sudarshan, Shridhar Sharma, Suyash Agrawal, Gayatri Kalshetti, Bindu Garg, and Rachna Jain. "Depression Level Analysis Using Face Emotion Recognition Method." In Proceedings of Data Analytics and Management, 265–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6550-2_21.
Full textTahmid, Marjana, Md Samiul Alam, Abhigna Bangalore Shreedhar, and Mohammad Kalim Akram. "Face Masks Use and Face Perception: Social Judgments and Emotion Recognition." In 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022”, 39–53. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25344-7_5.
Full textXia, Qingxin, Jiakang Li, and Aoqi Dong. "Road Rage Recognition System Based on Face Detection Emotion." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 174–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93479-8_11.
Full textBallihi, Lahoucine, Adel Lablack, Boulbaba Ben Amor, Ioan Marius Bilasco, and Mohamed Daoudi. "Positive/Negative Emotion Detection from RGB-D Upper Body Images." In Face and Facial Expression Recognition from Real World Videos, 109–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13737-7_10.
Full textConference papers on the topic "Face emotion recognition"
Panchal, S. S., Anand Hiremath, and Netra R. Toravi. "Automatic Face Emotion Recognition." In 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2018. http://dx.doi.org/10.1109/icssit.2018.8748847.
Full textDe Silva, Liyanage C., and Kho G. P. Esther. "Emotion-independent face recognition." In Photonics West 2001 - Electronic Imaging, edited by Bernd Girod, Charles A. Bouman, and Eckehard G. Steinbach. SPIE, 2000. http://dx.doi.org/10.1117/12.411839.
Full textEsau, Natascha, Lisa Kleinjohann, Bernd Kleinjohann, and Evgenija Wetzel. "A Fuzzy Emotion Model and Its Application in Facial Expression Recognition." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35206.
Full textGoodarzi, Farhad, Fakhrul Zaman Rokhani, M. Iqbal Saripan, and Mohammad Hamiruce Marhaban. "Mixed emotions in multi view face emotion recognition." In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. http://dx.doi.org/10.1109/icsipa.2017.8120643.
Full textNedkov, Svetoslav, and Dimo Dimov. "Emotion recognition by face dynamics." In the 14th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2516775.2516794.
Full textLi, Jiequan, and M. Oussalah. "Automatic face emotion recognition system." In 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS). IEEE, 2010. http://dx.doi.org/10.1109/ukricis.2010.5898118.
Full textTorres, Juan M. Mayor, and Evgeny A. Stepanov. "Enhanced face/audio emotion recognition." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3109423.
Full textShahabinejad, Mostafa, Yang Wang, Yuanhao Yu, Jin Tang, and Jiani Li. "Toward Personalized Emotion Recognition: A Face Recognition Based Attention Method for Facial Emotion Recognition." In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). IEEE, 2021. http://dx.doi.org/10.1109/fg52635.2021.9666982.
Full textVeltmeijer, Emmeke, Charlotte Gerritsen, and Koen Hindriks. "Automatic Recognition of Emotional Subgroups in Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/190.
Full textChang, Xin, and Wladyslaw Skarbek. "From face identification to emotion recognition." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2019. http://dx.doi.org/10.1117/12.2536735.
Full textReports on the topic "Face emotion recognition"
Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.
Full textClarke, Alison, Sherry Hutchinson, and Ellen Weiss. Psychosocial support for children. Population Council, 2005. http://dx.doi.org/10.31899/hiv14.1003.
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