Automatic Skin Color Beautification

Chen, Chih‐Wei; Huang, Deng-Yuan; Fuh, Chiou-Shann

doi:10.1007/978-3-642-11577-6_20

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…Chen et al [25] performed automatic skin color enhancement based on color temperature-insensitive skin color detection and editied faces using a bilateral filter with poisson image cloning.…”

Section: Facial Beautificationmentioning

confidence: 99%

Literature Review of Facial Skin Embellishment Using Flexible Region Aware Mask

2016

IJSR

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Facial attractiveness enhancement is becoming more and more important for beautification. So I am proposing flexible region aware mask technique for three major skin attributes: homogeneity, lighting, and color. For implementing such specific facial attractiveness, there is a new region-aware mask generation technique which is based on the edit propagation techniques. The regionaware masks allow the user to beautify faces .This paper propose an input image which is decomposed into three different layer by using edge-preserving filter. After decomposing the facial landmarks and significant edges as the restraint, layer masks which are generated based on the guided image of energy minimization technique. Our method could beautify faces automatically using the guided image. The result which has come based on the various databases.

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Section: Facial Beautificationmentioning

confidence: 99%

Literature Review of Facial Skin Embellishment Using Flexible Region Aware Mask

2016

IJSR

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“…The first category is to construct an automatic rendering model to generate specific facial effects. The related researches include the face relighting [PTMD07, CWJZ13], artistic illumination transfer [CJZW12], digital makeup [TTBX07, GS09], facial skin beautification [LJL14, LXL*14, CHF10], digital anti‐aging [Guo11], image‐based shaving [NLEDlT08], face swapping [BKD*08] and face reenactment [GVR*14], These methods share a similar processing workflow, which decomposes an input image into multiple layers, edits each layer according to different tasks, and finally composes edited layers to obtain the output. The above methods are effective for some specific facial effects, but it is difficult to obtain multiple facial effects in a unified framework simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Multiple Facial Image Editing Using Edge-Aware PDE Learning

Liang

Jin

Zhang

et al. 2015

Computer Graphics Forum

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Figure 1: Multiple face rendering effects generated in a unified framework using multiple edge-aware masks. AbstractThis paper introduces a novel facial editing tool, called edge-aware mask, to achieve multiple photo-realistic rendering effects in a unified framework. The edge-aware masks facilitate three basic operations for adaptive facial editing, including region selection, edit setting and region blending. Inspired by the state-of-the-art edit propagation and partial differential equation (PDE) learning method, we propose an adaptive PDE model with facial priors for masks generation through edge-aware diffusion. The edge-aware masks can automatically fit the complex region boundary with great accuracy and produce smooth transition between different regions, which significantly improves the visual consistence of face editing and reduce the human intervention. Then, a unified and flexible facial editing framework is constructed, which consists of layer decomposition, edge-aware masks generation, and layer/mask composition. The combinations of multiple facial layers and edge-aware masks can achieve various facial effects simultaneously, including face enhancement, relighting, makeup and face blending etc. Qualitative and quantitative evaluations were performed using different datasets for different facial editing tasks. Experiments demonstrate the effectiveness and flexibility of our methods, and the comparisons with the previous methods indicate that improved results are obtained using the combination of multiple edge-aware masks.

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“…Face manipulation has attracted great interests in computer vision and graphics [14,3,6,4,33,31,28]. Previous methods devoted to face beautification [19,8], de-beautification [10], expression manipulation [28] and age progression [14], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Facelet-Bank for Fast Portrait Manipulation

Chen

Lin

Shu

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smart phones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is necessary to accommodate different types of facial editing. In this paper, we propose a model to achieve this goal based on an end-to-end convolutional neural network that supports fast inference, edit-effect control, and quick partial-model update. In addition, this model learns from unpaired image sets with different attributes. Experimental results show that our framework can handle a wide range of expressions, accessories, and makeup effects. It produces high-resolution and high-quality results in fast speed.

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Automatic Skin Color Beautification

Cited by 15 publications

References 10 publications

Literature Review of Facial Skin Embellishment Using Flexible Region Aware Mask

Literature Review of Facial Skin Embellishment Using Flexible Region Aware Mask

Multiple Facial Image Editing Using Edge-Aware PDE Learning

Facelet-Bank for Fast Portrait Manipulation

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