摘要
为了更好地识别空中交通管制员疲劳状态下的面部特征,针对由于局部二值化模式(LBP)算法识别率低和易受外部环境变化等影响,深入研究了LBP算子、直方图特征提取对传统LBP算法改进。并结合了LIOP编码方法,进一步提出了增强局部量化模式(ELQP)算法。结果表明,改进后的LBP算法在管制员疲劳面部识别中具有更高的识别率和较强的鲁棒性。
In order to better identify the facial feature of air traffic controllers(ATC)in fatigue state,aiming at the low recognition rate and easy to be influenced by external environment changes of local binary pattern(LBP)algorithm,the improvements to traditional LBP algorithm caused by the LBP Operator and histogram feature extraction are deeply studied.Combining with the LIOP coding method,an enhanced local quantization pattern(ELQP)algorithm is proposed.The experiment results show that the improved LBP algorithm has higher recognition rate and stronger robustness in identifying ATC fatigue face.
作者
孙昕
杨昌其
陈连亮
Sun Xin;Yang Changqi;Chen Lianliang(Institute of Air Traffic Control,Civil Aviation Flight University of China,Guanghan,Sichuan 610000,China)
出处
《计算机时代》
2021年第8期67-70,共4页
Computer Era
基金
民航局安全能力项目“空管安全人员资质能力提升研究”。
关键词
管制员疲劳
LBP算法
特征提取
人脸识别
controller fatigue
LBP algorithm
feature extraction
face recognition
