International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Study of a Simplified Convolutional Neural Network for Thermal Face Recognition Tasks |
|---|---|
| ABSTRACT | This study explores the development of a lightweight and efficient deep learning approach for human face recognition, utilizing Convolutional Neural Networks (CNNs). The primary objective is to propose a model architecture that maintains high recognition accuracy while minimizing computational demands, making it suitable for real-time applications and resource-constrained environments. The concept involves training and evaluating the model on facial image datasets, incorporating essential preprocessing techniques such as image resizing and normalization to enhance performance. Although the model has not yet been implemented, the theoretical framework and design suggest that such a system could achieve accurate face recognition while operating efficiently on low-power or embedded systems. |
| AUTHOR | VARSHA KAILAS MALI, DR.S.N.KAKARWAL, KOMAL.D.GADEKAR |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311085 |
| pdf/85_Study of a Simplified Convolutional Neural Network for Thermal Face Recognition Tasks.pdf | |
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