Paper #1 - System for Recommending Facial Skincare Products
Key Notes
- Multi-feature processing and classification of skin quality and acne status
Approach
- K-means cluster to search for acne
- Binary threshold using an adaptive method
- Identified the location of acne and provided recommendations to consumers
- A scale of oiliness was produced by labeling images of the weighted average
Steps
- The first feature used to determine whether the skin is oily
- Acne detection
- The brightness image is subtracted from the normalized grayscale image
Paper #2 - A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images
Keynotes
- Acne is the 8th most common skin disorder in the world
- Transfer learning approach by extracting image features using a ResNet 152 pre-trained model, then adding and training a fully connected layer to learn the target severity level from labeled images.
- Mobile application for acne assessment
- Extracted skin patches from facial skins
- Haar feature-based cascade classifier
- Eye location, we inferred the regions of the forehead, cheeks and chin skin patches
Paper #3 - Deep Learning Methods for Selecting Appropriate Cosmetic Products for Various Skin Types: A Survey
Key Notes
- The cosmetic data from various websites @cosme and @Nykaa gathered for this model evaluation.
- The cosmetic product composition will be given based on skin types; dry, natural or oily.
Paper #4 Deep Learning Algorithms for Recognition of Facial Ageing Features
Keynotes
- Wrinkles, Dark spots, Under-eye circles
- Face Detection, wrinkle detection, scoring
- Facial zone - ensemble of regression trees, retrained for 50 fiducial points (dlib implementation) + contours detection
- Alignment - affine transformation
- Wrinkles area detection - cut areas by support points
More Reads
- Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
- Real-time Segmentation and Facial Skin Tones Grading
- Classification of skin pixels in images
Key Notes
- Spectral Residual (SR) - Approach based on Fast Fourier Transform (FFT). Key Steps are
- (1) Fourier Transform to get the log amplitude spectrum;
- (2) calculation of spectral residual; and
- (3) Inverse Fourier Transform that transforms the sequence back to the spatial domain
Visual saliency detection domain. Applying CNN on the basis of SR output directly
CNN as our discriminative model architecture
More Reads
Happy Learning!!!
