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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 114
Article Submitted : 18737
Article Published : 7882
Total Authors : 20831
Total Reviewer : 757
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Published Paper Details
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| Paper Title: | Facial Expression to Avatar |
| Authors Name: | Ms. Chaitra S , K S Vinay , M C V Ugandhar , Manit S Hegde , Nakul V |
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| Author Reg. ID: |
IJRTI_188843
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| Published Paper Id: | IJRTI2401017 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Facial expressions play an important role in human communication, conveying emotions and nuances that are integral to understanding one another. In the digital realm, emoticons have become a prevalent form of expressing emotions, yet translating facial expressions to emoticons accurately remains a challenge. This paper presents an approach with Convolutional Neural Networks (CNNs) to bridge this gap. Our proposed methodology involves a multi-stage process: facial expression recognition, feature extraction, and emoticon generation. Initially, a robust CNN model is employed to recognize and classify facial expressions from images or video frames into distinct emotional categories such as happiness, sadness, fear, disgust, anger, surprise and neutrality. Following successful classification, feature extraction techniques are applied to capture intricate facial details and nuances associated with each emotion. Subsequently, a mapping mechanism is devised to convert the extracted features into emoticons effectively. This involves a fusion of artistic design principles and computational algorithms to generate emoticons that encapsulate the identified emotions. The resulting emoticons are visually appealing and semantically aligned with the original facial expressions. Experimental evaluations conducted on benchmark datasets demonstrate the efficacy and accuracy of the proposed CNN-based framework in converting facial expressions to emoticons. Comparative analyses against existing methods reveal superior performance in terms of emoticon fidelity and emotional expression preservation. |
| Keywords: | Convolutional Neural Networks (CNN), Feature Extraction, Facial Recognition, Machine Learning, real-time processing, neural network, comparative analysis, Model evaluation. |
| Cite Article: | "Facial Expression to Avatar", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.90 - 97, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401017.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2401017
Registration ID:188843
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier):
Page No: 90 - 97 Country: Bangalore, Karnataka, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401017 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401017 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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