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dc.contributor.authorKhan, Zohaib Ahmad
dc.contributor.authorXia, Yuanqing
dc.contributor.authorAurangzeb, Khursheed
dc.contributor.authorKhaliq, Fiza
dc.contributor.authorAlam, Mahmood
dc.contributor.authorKhan, Javed Ali
dc.contributor.authorAnwar, Muhammad Shahid
dc.date.accessioned2024-04-25T08:15:02Z
dc.date.available2024-04-25T08:15:02Z
dc.date.issued2024-03-29
dc.identifier.citationKhan , Z A , Xia , Y , Aurangzeb , K , Khaliq , F , Alam , M , Khan , J A & Anwar , M S 2024 , ' Emotion detection from handwriting and drawing samples using an attention-based transformer model ' , PeerJ Computer Science , vol. 10 , e1887 , pp. 1/23 . https://doi.org/10.7717/peerj-cs.1887 , https://doi.org/10.7717/peerj-cs.1887
dc.identifier.issn2376-5992
dc.identifier.otherORCID: /0000-0003-3306-1195/work/158538218
dc.identifier.otherPubMedCentral: PMC11041987
dc.identifier.urihttp://hdl.handle.net/2299/27799
dc.description© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
dc.description.abstractEmotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).en
dc.format.extent23
dc.format.extent2394452
dc.language.isoeng
dc.relation.ispartofPeerJ Computer Science
dc.subjectBehavioral biometrics
dc.subjectEmotion detection
dc.subjectEmotional intelligence
dc.subjectEmotional state recognition
dc.subjectHandwriting/Drawing analysis
dc.subjectHuman-computer Interaction
dc.subjectTransformer model
dc.subjectGeneral Computer Science
dc.titleEmotion detection from handwriting and drawing samples using an attention-based transformer modelen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionBiocomputation Research Group
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionCybersecurity and Computing Systems
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85190278271&partnerID=8YFLogxK
rioxxterms.versionofrecord10.7717/peerj-cs.1887
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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