Beauty - Paper - Research Reads / Inspirations

Paper #1 - Modifying Face Image for Ageing Marks using Specialized Filter

• shrinking image - cv2.INTER_AREA
• stretching image - cv2.INTER_CUBIC

Key Notes

• Landmarks points [0-24] represent outer face region.
• Points [25-32] represent left eyebrow region. 
• Points [33-41] represent left eye region. 
• Points [45-51] represent right eyebrow region. 
• Points [52-59] represent right eye region.
• Points [ 68-78] represent lip regions and remaining points from [79-99] covers nose position. 
• Face mask is generated using convex hull Technique [1] using the outer face landmark points.

Transformations

• We use horizontal and vertical sobel filter for detecting the wrinkles in the specific region. 
• The average edge strength in each region is defined as the quantification of wrinkles feature.
• We apply a threshold condition to the edge intensity for getting the correct wrinkles from the image. 
• Main concentration will be on the forehead and eye corners

Paper # - BEHOLDER-GAN: GENERATION AND BEAUTIFICATION OF FACIAL IMAGES WITH CONDITIONING ON THEIR BEAUTY LEVEL

Key Notes

• Progressive Growing of GANs (PGGAN) [13] suggested coping with the challenge of generating high-resolution images by learning first through generation of low-resolution images and progressively growing to higher resolutions
• Another important aspect of GANs is their ability to generate images with conditioning on some attribute
• CycleGAN, StarGAN

Code - Link

• This is more useful for cheek/chin expansion

• To ensure that generated image x indeed corresponds to the correct beauty level Discriminator D predict the beauty level and not just the usual real vs. fake probability

Paper - Facial makeup transfer with GAN for different aging faces

Key Notes

• Firstly removing the eyebrows and eyelashes of the input image to prepare for the eye makeup transfer

• Since the information is transferred from pixel to pixel, it needs to be fully aligned before transfering, and then layer is decomposed by the Edge Preserving Smooth Filter

Paper - Facial Makeup Transfer Combining Illumination Transfer

• Facial makeup, eye shadow and lip makeup are processed by different loss functions, and the three are integrated
• OpenCV Bilateral Filtering Algorithm [12] to achieve facial smoothing

BasicSR (Basic Super Restoration) is an open-source image and video restoration toolbox based on PyTorch

•  Real-ESRGAN: A practical algorithm for general image restoration
•  GFPGAN: A practical algorithm for real-world face restoration
•  facexlib: A collection that provides useful face-relation functions.
•  HandyView: A PyQt5-based image viewer that is handy for view and comparison.
•  HandyFigure: Open source of paper figures

Paper # - Data Article template

Paper - Towards Real-World Blind Face Restoration with Generative Facial Prior

• GFPGAN consists of a degradation removal module and a pretrained face GAN as facial prior
• Facial component loss with local discriminators to further enhance perceptual facial details
• Image Restoration typically includes super-resolution, denoising, deblurring and compression removal
• Channel Split Operation is usually explored to design compact models and improve model representation ability

• latent features Flatent to map the input image to the closest latent code in StyleGAN2
• multi-resolution spatial features Fspatial for modulating the StyleGAN2 features.

Paper - SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

• We extracted 84 points as sample points containing facial contour information and shape information of the eyebrow, eyes, mouth, and so on
• The machinelearning methods we used include SVM regression (SVR), linear regression, pace regression, and Gaussian regression.

Dataset - Link

Paper - Improving Makeup Face Verification by Exploring Part-Based Representations

• The preprocessing step starts by applying a face detector followed by a 2D facial landmarks estimator, both available in DLib
• These landmarks are used to align, crop and resize the face thirds and facial parts
• left periocular, which includes the eye and eyebrow, right periocular, nose and mouth
• Makeup Face Dataset (EMFD)
• Youtube Makeup (YMU) Dataset [13]

FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces

• we prefer to enhance facial attractiveness via adjusting the relative distances among important facial components, such as eyes, nose, lip, and chin. 

BeautyGAN - BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network

BeautyGAN

Link1, Link2

Keep Exploring!!!