Metadata-Version: 2.1
Name: frapi
Version: 0.1.1
Summary: Package for Face Recognition API
Home-page: https://github.com/BIG-CHENG/FaceRec
Author: BIG CHENG
Author-email: bigcheng.asus@gmail.com
License: UNKNOWN
Description: # FaceRec
        
        簡單易懂，高精準度的人臉辨識技術封裝
        
        
        # Papers
        
        深度學習人臉辨識技術
        
        0. 基礎: 機器學習/深度學習/圖形處理器技術
        
        1. "DeepFace: Closing the Gap to Human-Level Performance in Face Verification"
        
        *https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.18.pm8zm1&file=Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf
        
        最早的深度學習人臉辨識, 已有 metric learning 的觀念 (使用 siamese network)
        
        但, 無權值共享的 CNN 帶來過多的參數, 3D alignment 也顯得過度複雜
        
        
        2. "Deep Face Recognition" 
        
        *http://cis.csuohio.edu/~sschung/CIS660/DeepFaceRecognition_parkhi15.pdf
        
        著名的 VGG Face, 整套流程包含 face dataset 的建立
        
        
        3. "FaceNet: A Unified Embedding for Face Recognition and Clustering"
        
        *https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf
        
        用 triplet loss 產生 128 維的 FaceNet embeddings (此向量空間內的距離代表人臉的相似程度), LFW 準確度超過 99%
        
        網路結構:
        
        101. (A) "Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations"
        
        *https://arxiv.org/pdf/1409.1556/
        
        經典的 VGG Network, 包含 VGG16, VGG19
        
        102. "Going Deeper With Convolutions"
        
        http://openaccess.thecvf.com/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
        
        GoogLeNet, 使用 3x3, 1x1 convolution 構成 inception 網路模組
        
        103. "Deep residual learning for image recognition"
        
        http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf
        
        residual network, 解決梯度消失問題, 讓訓練 100 (甚至1000) 層以上的深度學習變得容易
        
        104. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
        
        https://arxiv.org/abs/1704.04861
        
        mobile net, 小而快的網路， 但犧牲準確度， 
        
        A. "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments"
        
        http://cs.brown.edu/courses/cs143/2011/proj4/papers/lfw.pdf
        
        *著名的 lfw 人臉辨識準確率測試資料集
        
        # Results
        99%
        *https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_prec_recall_all.png
        ![LFW precision-recall ](https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_prec_recall_all.png)
        *https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_roc_all.png
        ![LFW ROC ](https://github.com/BIG-CHENG/FaceRec/blob/master/fr_lfw_roc_all.png)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
