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动态光照下驾驶人面部特征识别算法与试验研究

Keywords: 交通工程,眼鼻特征,机器视觉,AKF-HSI融合算法,照度

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Abstract:

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Gt(i,j)算子,实现驾驶室背景的分割;在HSI色彩空间中通过阈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kskin与眼鼻识别率δ,在2×104~10×104lx的照度下,眼鼻的平均识别率δ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。

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