摘要
基于机器视觉的驾驶人面部特征识别受光照的影响很大。为克服由于动态光照引起的背景干扰，面部特征弱化的问题，采用一种基于KalmanFiltering的光照自适应AKF算法，通过高斯概率密度函数建立Gi（i，j）算子，实现驾驶室背景的分割；在HSI色彩空间中通过闽值分割算法提取面部肤色区域，最终建立了眼鼻坐标搜索模型；进行了不同的照度与头部姿态下的AKF—HSI算法试验，测试统计前景分割率Kfrontground、肤色分割率kski．与眼鼻识别率δ，在2×10～10×10^4lx的照度下，眼鼻的平均识别率艿达到82％-92％。结果表明AKF—HSI融合算法对动态光照下眼鼻识别具有较好的鲁棒性，照度E、头部姿态与硬件设备AGC是眼鼻识别的最主要影响因素。
The recognition of driver＇s facial features based on machine vision is influenced greatly by illumination. In order to overcome dynamic illumination induced background interference and weakening of facial feature, a self-adaptive AKF algorithm based on Kalman filtering is adopted. Real-time segmentation of driving cab background is realized by Gi （i, j） operator established by Gaussian probability density function. By clustering performance of skin color and facial feature in HSI color space, skin region and facial feature are recognized and located by the threshold segmentation algorithm. Finally, an eye-nostril search model is set up by their coordinate value sequence. The AKF-HSI algorithm experiment is then carried out under different illuminancies and head poses. Frontground segmentation rate kfrontground, skin segmentation rate kskin and eye-nostril recognition rate 8 of different persons are obtained, which shows that the average recognition rate 8 reaches up to 82% - 92% with the outdoor illumination of 2 × 104 - 10 × l04 lx. The result shows that the anti-interference performance of AKF-HSI fusion algorithm is robust to eye-nostril recognition under dynamic illumination. Illuminancy E, head pose and AGC hardware equipment are the most important influence factors of eye-nostril recognition.
出处
《公路交通科技》
CAS
CSCD
北大核心
2014年第10期97-103,118,共8页
Journal of Highway and Transportation Research and Development
基金
交通运输部应用基础研究项目(2013319812150)
汽车运输安全保障技术交通行业重点实验室开放基金项目(2013G1502060
2013G15020)
教育部长江学者和创新团队发展计划项目(IRT1286)
关键词
交通工程
眼鼻特征
机器视觉
AKF—HSI融合算法
照度
traffic engineering
eye-nostril feature
machine vision
AKF-HSI fusion algorithm
illuminancy
