Aug 2026· Academic Journal of Electrical and Computer Engineering· Vol 2, pp. 54-59· 0 citations
TL;DR
This endeavor uses Transfer Learning to improve the performance of face-recognition systems, particularly when working with a limited dataset, and a pre-trained model known as Squeeznet CNN in MATLAB is employed.
Abstract
Face recognition stands as a crucial application of deep learning within computer vision, providing notable advantages in areas such as security, surveillance, and human-computer interaction. The process involves the identification of individuals through facial images. Recent progress in face recognition technology can be largely credited to deep learning techniques, especially convolutional neural networks (CNNs). This endeavor uses Transfer Learning to improve the performance of face-recognition systems, particularly when working with a limited dataset. To accomplish this, a pre-trained model known as Squeeznet CNN in MATLAB is employed.
A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.
Patel Bhautika Ronak· International journal of res...· 0 citations
A Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition.
K. Minney Prisilla, N. Jayashri· International journal of com...· 0 citations
A Deepfake detection framework based on MobileNetV2, which uses effective convolutional feature extraction to accurately classify real and fake facial photos to guarantee consistent input quality and improve the discriminative features for detection is suggested.
A. Mohitha, C. B. Jones· International Journal of Eng...· 0 citations
The novelty of this work lies in the integration of a lightweight CNN-LSTM framework with LDA to achieve a balance between high recognition accuracy and computational efficiency, making it suitable for real-time facial authentication in edge and IoT environments.
A hybrid model which uses several ResNet50 mixed with VGG16 backbones to improve the local facial features with the global context for a robust FER solu-tion, and shows superior robustness to occlusions and pose, even under cross-dataset testing.
A. Ouza, Mohamed El Ghmary, A. Choukri· Multimedia tools and applica...· 0 citations
The novelty of this work lies in the integration of a lightweight CNN-LSTM framework with LDA to achieve a balance between high recognition accuracy and computational efficiency, making it suitable for real-time facial authentication in edge and IoT environments.
Dalya Alkhafaji, W. Teahan· HighTech and Innovation Jour...· 0 citations
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