Computer Vision

Face Recognition: Deep Learning

DeepFace 1 Main idea Learn a deep (7 layers) NN (20 million parameters) on 4 million identity labeled face images directly on RGB pixels. Alignment Use 6 fiducial points for 2D warp

2021-02-15

Face Recognition: Features

Local Appearance-based Face Recognition 🎯 Objective: To mitigate the effect of expression, illumination, and occlusion variations by performing local analysis and by fusing the outputs of extracted local features at the feature or at the decision level.

2021-02-15

Face Recognition: Traditional Approaches

Face Recognition for Human-Computer Interaction (HCI) Main Problem The variations between the images of the same face due to illumination and viewing direction are almost always larger than image variations due to change in face identity.

2021-02-04

YOLOv3: Train on Custom Dataset

Training YOLOv3 as well as YOLOv3 tiny on custom dataset is similar to training YOLOv4 and YOLOv4 tiny. Only some steps need to be adjusted for YOLOv3 and YOLOv3 tiny:

2021-01-05

Scaled YOLOv4

Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao (more commonly known by their GitHub monikers, WongKinYiu and AlexyAB) have propelled the YOLOv4 model forward by efficiently scaling the network’s design and scale, surpassing the previous state-of-the-art EfficientDet published earlier this year by the Google Research/Brain team.

2021-01-05

YOLOv5: Train Custom Dataset

We will learn training YOLOv5 on our custom dataset visualizing training logs using trained YOLOv5 for inference exporting trained YOLOv5 from PyTorch to other formats. Clone YOLOv5 and install dependencies git clone https://github.

2020-12-25

YOLOv4: Training Tips

Model zoo YOLOv4 model zoo Pretrained models Proper configuration based on GPU We do NOT suggest you train the model with subdivisions equal or larger than 32, it will takes very long training time.

2020-12-19

Eigenface

Google Colab Notebook Open in Google Colab

2020-12-19

Modern Face Recognition Overview

Face recognition is a series of several related problems: Face detection: Look at a picture and find all the faces in it Focus on each face and be able to understand that even if a face is turned in a weird direction or in bad lighting, it is still the same person.

2020-12-19

Face

2020-12-19