Facial recognition with mask during pandemic period by big data technical of GMM

Abstract

At this pandemic period, for the safety demand of emigration, footprint tracking of disease carrier, pandemic control…etc., it is urgent as well as important to do an automatic recognition of a person with mask. This study uses Mel-frequency Cep-strum technic to simulate and extract human features; uses big data technician of supervising learning method and VQGMM to find out the impact factors of human features that affecting human recognition hit rate. This study using same algorithm to do four time of testing with mask and without mask. The study result show, after supervising training, the testing result of the people with mask is better than without mask which gave evidence of the algorithms of this study is robust.