Nannan Wang

TL;DR: This paper comprehensively surveys the development of face hallucination, including both face super-resolution and face sketch-photo synthesis techniques, and presents a comparative analysis of representative methods and promising future directions.

TL;DR: A comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images is presented in this article, where the authors categorize existing methods into two primary categories according to whether there is the need of a parametric shape model: Parametric Shape Model-based methods and Nonparametric Shape Models-Based methods.

TL;DR: This paper proposes an end-to-end dualstream hypersphere manifold embedding network (HSMEnet) with both classification and identification constraint and designs a two-stage training scheme to acquire decorrelated features.

TL;DR: Empirical results on benchmark-simulated and real-world label-noise datasets demonstrate that without using exact anchor points, the proposed method is superior to the state-of-the-art label- noise learning methods.

TL;DR: The proposed sketch-photo synthesis method works at patch level and is composed of two steps: sparse neighbor selection (SNS) for an initial estimate of the pseudoimage (pseudosketch or pseudophoto) and sparse-representation-based enhancement (SRE) for further improving the quality of the synthesized image.