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003 DZ-ElOued
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100 1 _aBoukhari, Djamel Eddine
700 1 _aChemsa, Ali
245 0 0 _aFace image analysis via Transformers: Algorithms and
260 _bUniversite Chahid Hamma Lakhdar d'El-Oued
_c2024
500 _aFace image analysis via Transformers: Algorithms and
520 _aThe perception of beauty has long been a central topic in human society, shaped by socioeconomic, cultural, and historical influences. Despite the evolving opinions on facial beauty worldwide, understanding the factors behind facial attractiveness remains a key area of interest across disciplines such as psychology, computer science, and evolutionary biology. With advancements in computer vision and deep learning, facial beauty prediction (FBP) has emerged as a cutting-edge field, enabling objective quantification of facial beauty and its underlying factors. This thesis proposes four novel approaches to facial beauty prediction using deep learning. Two approaches leverage convolutional neural networks (CNNs) integrated with ensemble learning, combining predictions from multiple models to improve accuracy. The remaining two approaches harness the power of Vision Transformers, utilizing attention mechanisms to capture intricate relationships within facial features. Together, these methods enhance feature representation and analysis for robust and reliable facial beauty assessment. Experiments conducted on the SCUT-FBP5500 benchmark dataset demonstrate the effectiveness of our approaches. We achieve superior performance by comparing various deep learning models, including AlexNet, ResNet-18, and ResNeXt-50. The proposed models yield predictions that closely align with human evaluations, surpassing conventional methods in accuracy and consistency. This thesis underscores the transformative impact of deep learning in facial beauty prediction, offering precise, unbiased, and automated evaluations of facial attractiveness.
650 4 _a/Face//image//analysis//via//Transformers//Algorithms//and/
942 _cTHESIS
999 _c18346
_d18346