# 03/31進度報告 ### 人臉辨識 training data *40 ![](https://i.imgur.com/f2zEx59.jpg =200x200)![](https://i.imgur.com/LN8FGXv.jpg =200x200) ![](https://i.imgur.com/BzSRBx5.jpg =200x200)![](https://i.imgur.com/TrdJSok.jpg =200x200) testing data *40 ![](https://i.imgur.com/VCN2wQl.jpg =200x200)![](https://i.imgur.com/kXLMomq.jpg =200x200) ![](https://i.imgur.com/3DTqgfr.jpg =200x200)![](https://i.imgur.com/HKZuDiO.jpg =200x200) training data *157 ![](https://i.imgur.com/aG34UIK.jpg =200x200)![](https://i.imgur.com/3UXlbfx.jpg =200x200) ![](https://i.imgur.com/d54OPcA.jpg =200x200)![](https://i.imgur.com/iHQqAme.jpg =200x200) --- ### 接下來規劃 1.用face detection擷取臉部 2.第一種Data：training data使用上面的157張，testing data使用上面的測試40張 3.第二種Data：將157張裡面的照片分出約40張作為testing data，並補上40張測試照片進去 --- ### paper--> **Deep RNN Framework for Visual Sequential Applications** http://openaccess.thecvf.com/content_CVPR_2019/papers/Pang_Deep_RNN_Framework_for_Visual_Sequential_Applications_CVPR_2019_paper.pdf