Deep learning for semantic description of visual human traits (2017)
Abstract / truncated to 115 words
The recent progress in artificial neural networks (rebranded as “deep learning”) has significantly boosted the state-of-the-art in numerous domains of computer vision offering an opportunity to approach the problems which were hardly solvable with conventional machine learning. Thus, in the frame of this PhD study, we explore how deep learning techniques can help in the analysis of one the most basic and essential semantic traits revealed by a human face, namely, gender and age. In particular, two complementary problem settings are considered: (1) gender/age prediction from given face images, and (2) synthesis and editing of human faces with the required gender/age attributes. Convolutional Neural Network (CNN) has currently become a standard model for image-based object ... toggle 8 keywordsdeep learning – soft biometrics – gender recognition – age estimation – aging/rejuvenation – gender swapping – CNN – GAN
- Antipov, Grigory
- Télécom ParisTech (Eurecom)
- Publication Year
- Upload Date
- Dec. 20, 2017
The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.
The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.