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
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