Learning Notes - Papers - Face Detection / Reidentification Papers

Paper #1 - Facial Keypoints Detection
Key Notes

• Using PCA and LBP 
• Apply different models
• Combine LBP and PCA together

Key Tasks

• Face Alignment
• Face Verification

Key Points locations

• lefteyecenter, righteyecenter,
• lefteyeinnercorner, lef teyeoutercorner,
• righteyeinnercorner, righteyeoutercorner,
• lefteyebrowinnerend, lef teyebrowouterend,
• righteyebrowinnerend, righteyebrowouterend,
• nosetip,
• mouthlef tcorner, mouthrightcorner,
• mouthcentertoplip, mouthcenterbottomlip

LBP (Local Binary Pattern) is an operator used to describe the local texture features of images. It has the  advantages of rotation invariance and gray invariance

Paper #2 - CNN architecture for Key Point Detection presented in Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

Paper #3 - FACE RECOGNITION SYSTEM 
Key Notes

• Face detection
• Face preprocessing 
• Face recognition processes

Facial feature points

• points (eyes, mouth center points, eyes, mouth contour points, organ contour points, etc.)

Siamese Network for Face Comparison

• Siamese network is neural network for measuring of similarity
• It can be used for category identification, classification

Paper #4 - Deep Face Recognition: A Survey

Key Notes

• Face detector is used to localize faces in images
• Facial landmark detector

Face Matching

• Cosine distance or L2 distance
• Nearest neighbor (NN) and threshold comparison 
• With GAN generate Makeup Faces, Similar Fake Faces and Compare

Paper #5 - A Fast and Accurate System for Face Detection, Identification, and Verification
Key Notes

• Deep CNN based detector

Face Detection

• Region proposal Networks
• Sliding window based

Multi-task learning for Facial Analysis

• Simultaneous face detection
• Landmark localization
• Headpose estimation

Single DCNN which can accomplish multiple tasks such as face detection, landmark localization, attribute prediction, age estimation, face recognition

More Reads
KPNet: Towards Minimal Face Detector
Face Recognition Based on the Key Points of High-dimensional Feature and Triplet Loss Automatic landmark annotation and dense correspondence registration for 3D human facial images
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition

Improving Makeup Face Verification by Exploring Part-Based Representations

Keep Thinking!!!