Pinned Repositories
CS-Xmind-Note
计算机专业课（408）思维导图和笔记：计算机组成原理（第五版 王爱英），数据结构（王道），计算机网络（第七版 谢希仁），操作系统（第四版 汤小丹）
Face-Recognition-system-using-parallel-computing
The face recognizing systems available so far maintain a database that has each pre-processed human face and their corresponding unique features as determined from each face. These features are stored with the respective individual’s face. Therefore, when a query is raised, the unique features are extracted from the face and then compared with the features in the database and results in a perfect match .There are many areas of applications that use face recognition technology ranging from security applications for recognizing criminals in public spaces such as airports, and shopping centers, verifying access to private property, and casting votes, to intelligent vision-based human computer interaction such as ATM, cell phones and intelligent buildings. Identifying the images and processing the data is a challenging task because of the various factors involved in this sophisticated process such as illumination, angle of pose, accessories, facial expression, and aging effects. There are two sets of data involved in the face recognition system. The first is the training set of data that is used in the learning stage. The second is a testing set which is used during recognition. Often real time response is understood to be in order of milliseconds and sometimes microseconds, which is the most crucial criteria in the system design. All in all, it can be said that this project is a good example of a parallel task with omp. Dealing with multiple pieces of data to read, calculate, and compare results takes a long time when tasks have to be performed sequentially.
wujiawei1114's Repositories
wujiawei1114/CS-Xmind-Note
计算机专业课（408）思维导图和笔记：计算机组成原理（第五版 王爱英），数据结构（王道），计算机网络（第七版 谢希仁），操作系统（第四版 汤小丹）
wujiawei1114/Face-Recognition-system-using-parallel-computing
The face recognizing systems available so far maintain a database that has each pre-processed human face and their corresponding unique features as determined from each face. These features are stored with the respective individual’s face. Therefore, when a query is raised, the unique features are extracted from the face and then compared with the features in the database and results in a perfect match .There are many areas of applications that use face recognition technology ranging from security applications for recognizing criminals in public spaces such as airports, and shopping centers, verifying access to private property, and casting votes, to intelligent vision-based human computer interaction such as ATM, cell phones and intelligent buildings. Identifying the images and processing the data is a challenging task because of the various factors involved in this sophisticated process such as illumination, angle of pose, accessories, facial expression, and aging effects. There are two sets of data involved in the face recognition system. The first is the training set of data that is used in the learning stage. The second is a testing set which is used during recognition. Often real time response is understood to be in order of milliseconds and sometimes microseconds, which is the most crucial criteria in the system design. All in all, it can be said that this project is a good example of a parallel task with omp. Dealing with multiple pieces of data to read, calculate, and compare results takes a long time when tasks have to be performed sequentially.