模式识别国家重点实验室筹建于1984年，1987年8月正式对外开放，同年12月通过国家验收，是由国家计委投资成立的国家重点实验室之一。实验室依托于中国科学院自动化研究所，第一任主任是前任国家科技部副部长马颂德研究员，现任主任是中国科学院副秘书长谭铁牛研究员。

        实验室以模式识别基础理论、图像处理与计算机视觉以及语音语言信息处理为主要研究方向，研究人类模式识别的机理以及有效的计算方法，为开发智能系统提供关键技术，为探求人类智力的本质提供科学依据。成立二十多年来，我们始终将“面向国家战略需求，瞄准国际学科前沿，开展模式识别领域的基础和应用基础研究，建设国际一流的科研团队，打造具有广泛影响力的模式识别领域国家公共研究平台，其成为国内外著名的科学研究、技术创新和人才培养基地，推动和引领模式识别学科的发展，为我国信息技术等战略新兴产业发展提供知识、技术与人才储备”作为实验室的总体定位与发展目标，努力将实验室建设成为一个高水平的、国际化的科研基地。

        实验室现有固定人员102人，45岁以下年青人约占80％，中科院“415计划”引进人才10人，国家杰出青年基金获得者5人，外籍职工2人。实验室流动人员包括博士后、博士研究生、硕士研究生、高级访问学者和客座研究人员，形成了一个以年轻学术带头人为核心、以青年科研骨干为主体和以高素质的研究生队伍为主力的研究梯队。

        实验室目前承担着近百项科研项目，其中包括国家重点基础研究计划“973”项目，国家自然科学基金重大、重点和面上项目、杰出青年科学基金项目，国家高技术计划“863”项目，国家科技支撑计划项目及国际合作项目等。

        实验室成员每年在国内外重要的学术期刊和国际学术会议上发表论文百余篇，其中不少发表在国际权威刊物和国际重要会议上，如IEEE汇刊（PAMI等），International Journal of Computer Vision, Pattern Recognition, Image and Vision Computing，Automatica, IEEE Transaction on Audio, Speech, and Language Processing, ICASSP, ICCV, ICPR, ACCV等。

        近年来，实验室已授权发明专利100余项，获国家自然科学二等奖1项，国家技术发明二等奖1项，国家科技进步二等奖2项，北京市科学技术奖一等奖2项，中科院自然科学二等奖2项，其它部委级三等奖4项，国际发明金奖与世界知识产权专项奖各1项。

         NLPR Overview

The National Laboratory of Pattern Recognition (NLPR) at the Institute of Automation of the Chinese Academy of Sciences（CAS）, was established in 1987 to become one of the first state key laboratories in China. It is conveniently located in Zhong Guan Cun, China’s “silicon valley”, in the capital city Beijing, and is easily reached to downtown areas and the Capital International Airport.

The NLPR has an energetic team of young talented researchers active in cutting-edge research in the broad field of Pattern Recognition, with specific interests in speech and language processing, image processing and computer vision, pattern recognition and biometrics. Currently, the faculty consists of 96 staff researchers supported by a team of 6 technicians and secretaries. Almost all staff researchers possess a PhD degree. The NLPR currently has 88MSc and 155 PhD students, 14 post-doctoral fellows and about 42 visiting researchers from China and overseas. The NLPR members publish widely in leading national and international journals and conferences such as IEEE Transactions on PAMI, IEEE Transactions on Medical Imaging, NeuroImage, Human Brain Mapping, Pattern Recognition, Pattern Recognition Letters, Eurospeech, ICASSP, ICSLP, ICCV, MICCAI, ACCV, ICPR, ICIP, etc. They are also active in program committees and editorial boards of these journals and conferences. The NLPR frequently organizes and hosts national and international conferences or workshops in its fields.

As a state key laboratory, the NLPR receives regular funding from various departments of the Chinese Government. It also attracts funds through R&D projects from many other national and international sources such as the National Natural Science Foundation of China, the National Hi-Tech R&D Program (the 863 Program), the National Key Fundamental Research Program (the 973 Program) and international co-operation programs. Currently, the NLPR undertakes more than 100 R&D projects.

 The NLPR is always keen on establishing academic links with world leading research laboratories. It currently hosts the Sino-French Laboratory for Computer Science, Automation and Applied Mathematics (LIAMA), Which was founded in 1997 and is jointly funded by the Chinese Academy of Sciences and INRIA, France. Each year, the LIAMA sponsors a number of research projects undertaken jointly by Chinese and French researchers. LIAMA is widely regarded as a flagship in international scientific cooperation. Each year NLPR members are seen at many international conferences and each year a large number of leading researchers from around the globe come and visit the NLPR.

NLPR’S MISSION

l  Conduct cutting-edg research in the broad field of Pattern Recognition

l  Establish a center of excellence in scientific research, technological innovation and postgraduate education

l  Promote technology transfer and international collaboration

RESEARCH DIRECTIONS

l  Pattern Recognition

l  Image Processing and Computer Vision

l  Speech and Language Processing

l  Biometrics