Transfer Learning for Facial Attributes Prediction and Clustering


Notwithstanding the enhancement obtained in the last decade researches, the recognition of facial attributes is still today a trend. Besides the mere face recognition, the singular face features, like mouth, nose and hair, are considered as soft biometrics; these can be useful for human identification in cases the face is partially occluded, and only some regions are visible. In this paper we propose a model generated by transfer learning approach for the recognition of the face attributes. Also, an unsupervised clustering model is described, which is in charge of dividing and grouping faces based on their characteristics. Furthermore, we show how clusters can be evaluated by a compact summary of them, and how Deep Learning models should be properly trained for attribute prediction tasks.

Communications in Computer and Information Science Volume 1122 CCIS, 2019, Pages 105-117 7th International Conference on Smart City and Informatization, iSCI 2019; Guangzhou; China; 12 November 2019 through 15 November 2019; Code 233889
Silvio Barra
Silvio Barra
Assistant Professor

Assistant Professor @ University of Naples