Efficient deep discriminant embedding: Application to face beauty prediction and classification

Inspired by deep learning architectures, we introduce a multi-layer local discriminant embedding algorithm that integrates feature selection as a main step to capture the most relevant and discriminant features of an input face image or face descriptor. The proposed framework allows to transform any linear method to a deep variant via a cascaded feature extraction and selection architecture able to convert weak and noisy descriptors to strong ones. As a case study, the local discriminant embedding (LDE) projection is adopted as a linear feature extraction method. The resulting framework can be considered as an efficient deep discriminant embedding technique. To validate this framework, we have considered two different computer vision problems: face beauty prediction which involves both classification and regression tasks, and face recognition which is a classical classification problem. Experiments conducted on different public benchmark databases show that this approach enhances the performance of the LDE algorithm and provides a discriminating strategy to solve the dimensionality reduction problem. For face beauty regression, our proposed framework achieved on average an improvement of about 5% and 7% with respect to two other configurations where only VGG-face and VGG-face followed by LDE have been considered. For face beauty classification, the proposed algorithm outperformed many classical manifold learning techniques reaching in some databases improvements of about 10%.