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8篇 您的检索式：作者名="Guangda Su"

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		题名	作者	年代	出处	被引量
	1	Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment显示文摘Sparse Representation based Classification (SRC) has emerged as a new paradigm for solving recognition problems. This paper presents a constraint sampling feature extraction method that improves the SRC recognition rate. The method combines texture and shape features to significantly improve the recognition rate. Tests show that the combined constraint sampling and facial alignment achieves very high recognition accuracy on both the AR face database (99.52%) and the CAS-PEAL face database (99.54%).	Jing Wang Guangda Su Ying Xiong Jiansheng Chen Yan Shang Jiongxin Liu Xiaolong Ren	2013	Tsinghua Science and Technology2013，18，1：	6
	2	Eyeglasses removal from facial images显示文摘	Du Cheng Su Guangda	2005	Pattern Recognition Letters2005，26，14：	1
	3	Doublepupil Location of Face Images显示文摘	Zhang Gang Chen Jiansheng Su Guangda	2013	Pattern Recognition2013，46，3：	1
	4	MMP-PCA face recognition method 显示文摘	SU Guangda ZHENG Cuiping DING Rong DU Cheng	2002	Electronics Letters2002，38，25：	1
	5	MMP-PCA face recognition method 显示文摘	SU Guangda ZHANG Cuiping DING Rong	2002	Electronics Letters2002，38，5：	1
	6	MMP-PCA face recognition method显示文摘	SU Guangda ZHANG Cuiping DING Rong	2002	Electronics Letters2002，38，25：	1
	7	Eyeglasses removal from facial images显示文摘	DU Cheng SU Guangda	2005	Pattern Recognition Letters2005，26，14：	1
	8	High-speed reconstruction for ultra-low resolution faces显示文摘In this paper,a learning-based high-speed reconstruction system for ultra-low resolution faces is implemented using a software/hardware co-design paradigm.The hardware component working at 60 MHz contains a field programmable gate array,which is reconfigured to contain parallel processing units,and multiple memories to create parallel data.The hardware component effectively handles generating and sorting computationally intensive similarity metrics.This solves the processing speed problem in learning-based super-resolution reconstruction for ultra-low resolution faces.The system can reconstruct faces using 8 × 6,16 × 12,and 32 × 24 sized images,with 4 × 4,8 × 8,or 16 × 16 times magnification.The experimental results verify the effectiveness of our system in terms of both visual effect and low root mean square errors.The processing speed can be improved up to a maximum of 7900 times faster than a pure software implementation using C.	WANG Li CHEN JianSheng HE JinPing SU GuangDa	2012	Science China(Information Sciences)2012，55，9：	1

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