为提高出入境自助通关系统中THFaceID人脸认证算法的性能,分析通关系统中的运行环境、旅客通关习惯等影响因素,针对模糊、多姿态和复杂光照下的现场人脸图像,提出一种多形状局部二值模式(multi shape local binary pattern,MS-LBP)特征,能够较好描述特殊结构的非矩形纹理,对多姿态人脸进行有效检测。使用异方差方式进行鉴别特征的提取,将现场照与备案照的特征向量变换到同一特征空间进行认证。实验结果表明,升级后核心算法的认证性能在FAR=0.001处比原版本系统有近20%的提高。
Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling(AAR). To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks(CNNs) is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue" aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units(GPUs), a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method's accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.
Wang XufengDong XinminKong XingweiLi JianminZhang Bo
