A Novel Frontal View Synthesis Method Based on Neighbor Embedding
This paper presents a novel approach that can efficiently synthesize a virtual frontal view, given only a single non-frontal face image. A non-frontal face image is separated into shape and shape-free texture, and Neighbor Embedding (NE) is applied to them respectively. The virtual frontal face can be generated by warping the shape-free texture to the shape and enforcing local compatibility and smoothness constraints between adjacent patches. While our method resembles other learning-based methods in relying on a training set, our method is novel in that it accurately reveals the intrinsic distribution of different pose feature spaces by assuming that the feature spaces for the frontal and non-frontal face images share similar local manifold structure. Experimental results show that the proposed method is better than Linear Object Classes (LOC) based method and Tensor-based Subspace Learning (TSL) method, both in the subjective and objective.
frontal view synthesis neighbor embedding manifold learning afflne transform
Zhen Han Junjun Jiang Ruimin Hu Tao Lu
School of Computer, Wuhan University, Wuhan, 430072, China National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
国际会议
武汉
英文
128-132
2011-10-21（万方平台首次上网日期，不代表论文的发表时间）