Face Recognition
This introduction includes Face Datasets, Face Detection, Face Alignment, Face Landmark, Face Recognition, Face Identification and Exercises.
Face Datasets
VGGFace
vgg_face_dataset.tar.gz
The VGG Face dataset is face identity recognition dataset that consists of 2,622 identities. It contains over 2.6 million images
VGGFace2
Paper: VGGFace2: A dataset for recognising faces across pose and age
CelebA
Large-scale CelebFaces Attributes dataset
Paper: Deep Learning Face Attributes in the Wild
LFW
Labeled Faces in the Wild Home (lfw.tgz)
Paper: Labeled Faces in the Wild: A Database for Studying
Face Recognition in Unconstrained Environments
The LFW dataset contains 13,233 images of faces collected from the web. This dataset consists of the 5749 identities with 1680 people with two or more images. In the standard LFW evaluation protocol the verification accuracies are reported on 6000 face pairs.
WebFace260M
Face Detection
MTCNN
Paper: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Code: ipazc/mtcnn
$pip install mtcnn
DeepFace
Code: RiweiChen/DeepFace
5 key points detection using DeepID architecture
Face Alignment
Face-Alignment
Code: 1adrianb/face-alignment
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
input = io.imread('../test/assets/aflw-test.jpg')
preds = fa.get_landmarks(input)
OpenFace
Code: cmusatyalab/openface
Ref. Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
Face Landmark
Face Landmark Estimation
Paper: One Millisecond Face Alignment with an Ensemble of Regression Trees
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face
3D Face Reconstruction & Alignment
Paper: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Code: YadiraF/PRNet
3D Head Pose Estimation
EfficientFAN
Paper: EfficientFAN: Deep Knowledge Transfer for Face Alignment
Face Recognition
Paper: A Survey of Face Recognition
DeepID
Paper:DeepID3: Face Recognition with Very Deep Neural Networks
Code: hqli/face_recognition
Blog: DeepID人臉識別算法之三代
Ref. DeepID3 face recognition
DeepID3在LFW上的face verification準確率爲99.53%,性能上並沒有比DeepID2+的99.47%提升多少。而且LFW數據集裏面有三對人臉被錯誤地標記了,在更正這些錯誤的label後,兩者準確率均爲99.52%。
FaceNet
Paper: FaceNet: A Unified Embedding for Face Recognition and Clustering
Code: https://github.com/davidsandberg/facenet
FaceNet-with-TripletLoss
Face Recognition Using Pytorch
VarGFaceNet
Paper: VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
Code: https://github.com/zma-c-137/VarGFaceNet
MogFace
Paper: MogFace: Towards a Deeper Appreciation on Face Detection
Code: https://github.com/damo-cv/MogFace
MixFaceNets
Paper: MixFaceNets: Extremely Efficient Face Recognition Networks
Code: https://github.com/fdbtrs/mixfacenets
EfficientFace
Paper: EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection
Code: https://github.com/zengqunzhao/EfficientFace
TransFace
Paper: TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective
Code: https://github.com/DanJun6737/TransFace
Efficient Face Recognition Competition at IJCB2023
Paper: EFaR 2023: Efficient Face Recognition Competition
Face Identification (人臉辨識)
FaceNet-PyTorch
Kaggle: FaceNet-PyTorch
dists = [[(e1 - e2).norm().item() for e2 in embeddings] for e1 in test_embeddings]
for dist in dists:
if any(e<1 for e in dist):
print(names[np.argmin(dist)])
else:
print('unknown')
Open-Set One-Shot Face Recognition
Code: https://github.com/robertanto/Open-Set-One-Shot-Face-Recognition
Exercises
Face Mask Detection (口罩偵測)
Using MTCNN to detect face, then use CNN model to detect facemask
Sync-up Tensorflow version on Kaggle and PC
On Kaggle:
import tensorflow as tf
print(tf.__version__)
2.6.1
On PC:
$python -c 'import tensorflow as tf; print(tf.__version__)'
2.6.1
Make sure to use same version of Tensorflow on Kaggle and PC for model file compatibilty !
Train Model on Kaggle
Kaggle: rkuo2000/faskmask-cnn
Dataset: [Face Mask Detection ~12K Images Dataset](https://www.kaggle.com/ashishjangra27/face-mask-12k-images-dataset
- [Edit & Copy] FaceMask-CNN
- [Run All] to Train model
- Download model file
facemask_cnn.h5
Run Model Prediction on PC
- Clone sample codes
$git clone https://github.com/rkuo2000/tf
- Copy facemask_cnn.h5 to ~/tf/models
- To detect using image file
$python mtcnn_facemask_detection.py images/facemask1.jpg
- To detect using webcam
$python mtcnn_facemask_detection_cam.py
[Homework]: Face Detection
Code: https://github.com/derronqi/yolov8-face
Dataset: WiderFace
YOLOv8n-face
[Homework]: Face Identification
Kaggle: FaceNet-PyTorch
Steps:
- [Fork] https://github.com/rkuo2000/facenet-pytorch
- upload 1.jpg for each name to
https://github.com/your_name/facenet-pytoch/data/test_images
- upload yourself photo 1.jpg to
https://github.com/your_name/facenet-pytoch/data/test_images/your_name
- [Edit&Copy] https://kaggle.com/rkuo2000/facenet-pytorch
- modify git clone path
!git clone https://github.com/your_name/facenet-pytorch
- modify test image path
img = plt.imread("facenet_pytorch/data/test_images/your_name/1.jpg")
- [Run-All]
This site was last updated December 15, 2024.