A Deep Learning–Based Framework for Feature Extraction and Facial Verification
Abstract
With an emphasis on how Convolutional Neural Networks (CNNs) improve accuracy, adaptability, and efficiency over conventional techniques, this study investigates the incorporation of deep learning techniques in facial recognition. The paper highlights the deep learning process by describing procedures, including face identification, alignment, feature extraction, and recognition. CNNs' ability to derive intricate patterns from unprocessed image data is one of their main advantages; this enables reliable feature extraction and precise detection even in situations with changing illumination, attitude, and occlusion. Along with discussions of exciting future advancements meant to enhance fairness, robustness, and privacy preservation within facial recognition systems, challenges such as data bias, privacy problems, and adversarial susceptibility are highlighted.
Keywords:
Convolutional neural networks, Deep learning in facial recognition, Feature extraction, Image data processing, Privacy preservation, Future advancementsReferences
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