Research and implementation of intelligent terminal lightweight face recognition system - Guo-jun MA; Hai-dong ZHOU (Tongxin xuebao)
In order to solve this problem that traditional face recognition scheme was not efficiently suitable to intelligent terminal scene.An improved lightweight scheme of feature weighted clustering matching based on SIFT was presented.The scheme can learn automatically and adaptively add test samples to the training sample space.Reasonable division and scientific distribution of weight make this scheme has improved at run time and recognition rate.The improved algorithm has been tested in the ORL face database and Yale face database,compared with the classical SIFT algorithm the recognition rate improved by 6.13% and 14.11%,the running efficiency increased 9.1% and 4.7%.At the same time,in accordance with the test method by Zhou,in the ORL face database,the recognition rate was up to 74.05%,significantly improved than PCA,LBP and other classical algorithm.The algorithm in Android terminal is implemented,and the improved algorithm is verified to be available in the Android system by experiments.Finally,an improved scheme was proposed based on the cloud architecture.