https://github.com/IsharaSandun/Face-Recognition-using-python
FaceNet: A Unified Embedding for Face Recognition and Clustering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deep High-Resolution Representation Learning for Visual Recognition
High-Resolution Representations for Labeling Pixels and Regions
Deep Alignment Network: A convolutional neural network for robust face alignment
RetinaFace: Single-stage Dense Face Localisation in the Wild
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
FaceNet: A Unified Embedding for Face Recognition and Clustering
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PCANet: A Simple Deep Learning Baseline for Image Classification?
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.