Thesis Defense of Rola El Saleh
Rola El Saleh, a PhD candidate from the SYNAPSE team, defended her thesis on December 16, 2021, in the RT Amphitheater at the Vitry-sur-Seine campus of UPEC—120 rue Paul Armangot, 94400 Vitry-sur-Seine.
Title: Biometrics for Face Skin Analysis Using Machine Learning-Based Approaches
Abstract:
The emergence of artificial intelligence (AI), access to large databases, and the availability of supercomputers have undeniably revolutionized various fields. In particular, the development of machine learning (ML) algorithms, especially deep learning (DL), has greatly benefited the biomedical domain. In the context of dermatology, numerous studies have been conducted to automatically analyze skin images to predict diseases and monitor their progression over time.
This thesis proposes a computer-aided diagnostic system based on DL approaches that analyzes facial images and identifies potential facial diseases using only facial phenotypes, without region of interest extraction. This medical facial biometrics relies on the use of pre-trained convolutional neural networks (CNNs) such as VGG-16, EfficientNet B0, and Inception V3, which are fine-tuned to create new models tailored for classifying facial skin images into eight distinct pathologies: acne, actinic keratosis, angioedema, blepharitis, eczema, melasma, rosacea, and vitiligo.
To achieve this, a transfer learning method is utilized. Specifically, the original architectures of the three models are modified by adding new layers at the top. The proposed algorithms are trained and validated on a database specifically created for this purpose. The models are tested and evaluated under varying acquisition conditions (facial pose, lighting, image resolution, etc.). The results obtained are very promising, demonstrating the effectiveness of the proposed approach in accurately diagnosing facial skin diseases.