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1. CN108427942 - Palm detecting and key point location method based on deep learning

Office China
Application Number 201810363952.8
Application Date 22.04.2018
Publication Number 108427942
Publication Date 21.08.2018
Publication Kind A
IPC
G06K 9/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G06K 9/00013
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00006Acquiring or recognising fingerprints or palmprints
00013Image acquisition
G06K 9/00067
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00006Acquiring or recognising fingerprints or palmprints
00067Preprocessing; Feature extraction (minutiae)
G06K 9/00087
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00006Acquiring or recognising fingerprints or palmprints
00087Matching; Classification
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
Applicants GUANGZHOU MELUX INFORMATION TECHNOLOGY CO., LTD.
广州麦仑信息科技有限公司
Inventors XIE QINGLU
谢清禄
YU MENGCHUN
余孟春
ZOU XIANGQUN
邹向群
WANG XIANFEI
王显飞
Title
(EN) Palm detecting and key point location method based on deep learning
(ZH) 一种基于深度学习的手掌检测与关键点定位方法
Abstract
(EN)
The invention discloses a palm detecting and key point location method based on deep learning. The palm detecting and key point location method based on deep learning particularly includes the following steps of 1, collecting training samples; 2, building network models, wherein a CNN feature extraction network, an RPN candidate area extraction network and a judgment network are built; 3, trainingthe network models, wherein the CNN feature extraction network, the RPN candidate area extraction network and the judgment network are initialized; 4, building a detection model, wherein the CNN feature extraction network, the RPN candidate area extraction network and the judgment network form a Faster R-CNN detection network; 5, detecting the palm and location key points. According to the palm detecting and key point location method based on deep learning, the Faster R-CNN detection framework which has optimal performance and accuracy at present is adopted, compared with a Fast R-CNN, a RPNis adopted to replace a Selective Search method to extract candidate areas, the RPN is completely built in the whole target detection framework, therefore, the speed of extracting the candidate areasis increased, and at the same time, the detection accuracy is improved.

(ZH)
本发明公开了一种基于深度学习的手掌检测与关键点定位方法,该基于深度学习的手掌检测与关键点定位方法具体步骤如下:S1、采集训练样本;S2、构建网络模型:构建CNN特征提取网络、RPN候选区域提取网络和判别网络;S3、训练网络模型:初始化CNN特征提取网络、RPN候选区域提取网络和判别网络;S4、构建检测模型:将CNN特征提取网络模型、RPN候选区域提取模型和判别网络组合成一个Faster R‑CNN检测网络;S5、手掌检测与关键点定位。利用了目前性能和准确度最佳的检测框架Faster R‑CNN,相比Fast R‑CNN,它使用RPN网络替换了Selective Search的方法提取候选区域,RPN网络完全嵌入到整个目标检测框架中,在加快候选区域提取速度的同时提高了检测的准确率。