From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle

---
 README.md |   27 +++++++++++----------------
 1 files changed, 11 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index 8dafd94..978d3ab 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 
 **Hopenet** is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.
 
-For details about the method and quantitative results please check the [paper](https://arxiv.org/abs/1710.00925).
+For details about the method and quantitative results please check the CVPR Workshop [paper](https://arxiv.org/abs/1710.00925).
 
 <div align="center">
 <img src="conan-cruise.gif" /><br><br>
@@ -41,29 +41,24 @@
 
 Please open an issue if you have an problem.
 
-Some very cool implementation of this work on other platforms by some cool people:
+Some very cool implementations of this work on other platforms by some cool people:
+
 [Gluon](https://github.com/Cjiangbpcs/gazenet_mxJiang)
+
 [MXNet](https://github.com/haofanwang/mxnet-Head-Pose)
+
 [TensorFlow with Keras](https://github.com/Oreobird/tf-keras-deep-head-pose)
 
 
 If you find Hopenet useful in your research please cite:
 
 ```
-@article{DBLP:journals/corr/abs-1710-00925,
-  author    = {Nataniel Ruiz and
-               Eunji Chong and
-               James M. Rehg},
-  title     = {Fine-Grained Head Pose Estimation Without Keypoints},
-  journal   = {CoRR},
-  volume    = {abs/1710.00925},
-  year      = {2017},
-  url       = {http://arxiv.org/abs/1710.00925},
-  archivePrefix = {arXiv},
-  eprint    = {1710.00925},
-  timestamp = {Wed, 01 Nov 2017 19:05:43 +0100},
-  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1710-00925},
-  bibsource = {dblp computer science bibliography, http://dblp.org}
+@InProceedings{Ruiz_2018_CVPR_Workshops,
+author = {Ruiz, Nataniel and Chong, Eunji and Rehg, James M.},
+title = {Fine-Grained Head Pose Estimation Without Keypoints},
+booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+month = {June},
+year = {2018}
 }
 ```
 

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