[1]XIA Yangyang,GONG Xun,HONG Xijin.Research on the data cleansing problem for face recognition technology[J].CAAI Transactions on Intelligent Systems,2017,12(5):616-623.[doi:10.11992/tis.201706025]
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Research on the data cleansing problem for face recognition technology

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 616-623 Column: 学术论文—机器感知与模式识别 Public date: 2017-10-25
Title:
Research on the data cleansing problem for face recognition technology
Author(s):
XIA Yangyang1;  GONG Xun1;  HONG Xijin1; 2
1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China;
2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10607, China
Keywords:
deep convolution neural network; DCNN; cleansing image; face recognition; large database
CLC:
TP391.4
DOI:
10.11992/tis.201706025
Abstract:
Face recognition technology has made a significant progress in the rapid development of deep convolution neural networks (DCNN). These developments are mainly focused toward a denser DCNN architecture and larger training database. However, DCNN training is affected because the large-scale database held by most private companies are not publically accessible. Moreover, current large-scale open databases are not accessible because of the slight availability of the labeled information and hard-to-guarantee accuracy. This study presents an easy-to-use image cleansing method to improve the accuracy of data from the following perspectives:First, deleting the face image that cannot be detected by face detection; second, using the existing model to extract the features of an image on the cleaned dataset and calculate the similarity; and finally, counting the number of images that are unlike the other images. The data were cleansed according to the improved parameters extracted from the abovementioned perspectives. The experimental results reveal that the cleansed database training model has improved the accuracy of face recognition in LFW(labeled faces in the wild) and YouTube face database. In the case of using a small-scale dataset, an accuracy of 99.17% and 93.53% was achieved on the LFW and YouTube face datasets, respectively.
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