Abstract
Face images in different modalities are often encountered in many applications, such as face image in photo and sketch style, visible light and near-infrared style. As an active yet challenging task, cross-modality face synthesis aims to transform face images between modalities. Many existing methods successfully recover global features for a given photo, however, fail to capture fine-scale details in the synthesis results. In this paper, we propose a two-step algorithm to tackle this problem. Firstly, KNN is used to select the K most similar patches in training set for an input patch centered on each pixel. Then combination of patches is calculated for initial results. In the second step, guided image filtering is used on initial results with test photo as guidance. Fine-scale details can be transferred to the results via local linear transformation. Comparison experiments on public datasets demonstrated the proposed method is superior to the state-of-the-art method in simultaneously keeping global features and enhancing fine-scale details.
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Dang, Y., Li, F., Li, Z., Zuo, W. (2015). Detail-Enhanced Cross-Modality Face Synthesis via Guided Image Filtering. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_20
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DOI: https://doi.org/10.1007/978-3-662-48558-3_20
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