CAJ | 학술논문

针对人脸识别在学生刷卡系统中的应用要求，选用Fishface方法进行图像处理。处理过程：设有训练样本S，通过S找到一个可以降低识别复杂性和降低噪音的变换，把得到的标有名字的人脸图像经过这种变换后存储到数据库中。识别一个人脸图像时，将图像进行相同的变换处理，用得到的结果与数据库中的各个人脸样本比较，求出欧氏距离或者马氏距离，与最小距离相对应的那个人的名字就是输出结果。并将算法移植到嵌入式ARM系统中，实现学生人脸的动态采集和识别，对30个人脸样本测试后，识别率达到87．502％。
침대인검식별재학생쇄잡계통중적응용요구，선용Fishface방법진행도상처리。처리과정：설유훈련양본S，통과S조도일개가이강저식별복잡성화강저조음적변환，파득도적표유명자적인검도상경과저충변환후존저도수거고중。식별일개인검도상시，장도상진행상동적변환처리，용득도적결과여수거고중적각개인검양본비교，구출구씨거리혹자마씨거리，여최소거리상대응적나개인적명자취시수출결과。병장산법이식도감입식ARM계통중，실현학생인검적동태채집화식별，대30개인검양본측시후，식별솔체도87．502％。
Aiming at the application requirements for the face recognition system of student credit card, the paper selects Fishface methods for image processing. That is ： setting a system for training sample S, by S to find a transformation to reduce the complexity of recognition and noise, then this face image to be marked with the name is stored in the database after conversion. When a face image is to be identified, the makes a same image transformation processing, compared with each individual face in database, find the Euclid- ean distance or Mahalanobis distance, then the person name that corresponding to the minimum distance is the result. The algorithm is also repotted to the embedded ARM system to achieve a dynamic collection of students and face recognition. The test with 30 individual face samples shows that the recognition rate is of 87. 502%.