Authors:
Emna Ghorbel
;
Ghada Maddouri
and
Faouzi Ghorbel
Affiliation:
CRISTAL Laboratory, GRIFT Research Group ENSI, La Manouba University 2010, La Manouba, Tunisia
Keyword(s):
Low Size Dataset, Face Data Augmentation, InceptionV3, Face Classification.
Abstract:
Facial image classification plays a vital role in computer vision applications, particularly in face recognition. Convolutional Neural Networks have excelled in this domain, however, their performance decline when dealing with small facial datasets. In that context, data augmentation methods have been proposed. In line with this, we introduce the Face Blending data augmentation method, which augments intra-class variability while preserving image semantics. By interpolating faces, we generate non-linear deformations, resulting in in-between images that maintain the original’s global aspect. Results show that Face Blending significantly enhances facial classification. Comparisons with Mix-up and Random Erasing techniques reveal improved accuracy, precision, recall, and F1 score, particularly with limited datasets. This method offers promise for realistic applications contributing to more reliable and accurate facial classification systems with limited data.