Open AccessFace Recognition Based on Haar Like and Euclidean Distance
Author(s) -
Hao Wu,
Yu Cao,
Haiping Wei,
Zhuang Tian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012036
Subject(s) - artificial intelligence , pattern recognition (psychology) , facial recognition system , euclidean distance , haar like features , pixel , computer science , classifier (uml) , feature (linguistics) , face (sociological concept) , haar , computer vision , face detection , mathematics , social science , linguistics , philosophy , sociology , wavelet
In order to improve the recognition rate of multi-face image, this paper proposes a face image recognition method based on haar-like and Euclidean distance. First of all, the initial features of Haar are moved in the image and gradually enlarged, the pixel sum of the feature area is obtained quickly by using the integral graph. The Haar feature is obtained by calculating the difference between black and white pixels in the feature region, and the threshold of weak classifier is calculated. Then, the extracted facial feature data is trained and classified to form a cascade classifier for face detection. Finally, the face of the image to be detected is compared with the training samples by using Euclidean distance. The experimental results show that the time of one attendance is 5-10 seconds and the success rate of face recognition is 91.1%, which verifies the advantage of the algorithm in improving the efficiency of attendance.