@inproceedings{c7c704b2682143609ecefe6a69029a3a,
title = "A face detection method based on LAB and adaboost",
abstract = "Face detection has been a hotspot either in research and in commercial application. In this paper, Locally Assembled Binary (LAB) feature and Adaboost algorithm are combined to recognize human face in images. On the basis of ensuring the detection speed, the detection accuracy is improved. Integral image technology is also conducted in consideration of detection speed. The proposed method is tested on CMU and FDDB face databases. On CMU, the result of the test indicates that the true positive rate is about 85% and the false positive rate is about 1%. The true positive rate is about 72.9% on FDDB. This method is much more better than Viola-Jones method.",
keywords = "Adaboost, Classifier, Face detection, LAB feature",
author = "Jiayao Bi and Jianqiang Chen and Shu Yang and Chengcai Li and Jing Wang and Bo Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th International Conference on Virtual Reality and Visualization, ICVRV 2016 ; Conference date: 24-09-2016 Through 26-09-2016",
year = "2017",
month = jun,
day = "1",
doi = "10.1109/ICVRV.2016.37",
language = "English",
series = "Proceedings - 2016 International Conference on Virtual Reality and Visualization, ICVRV 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "175--178",
editor = "Dandan Ding and Dangxiao Wang and Jian Chen and Xun Luo",
booktitle = "Proceedings - 2016 International Conference on Virtual Reality and Visualization, ICVRV 2016",
address = "United States",
}