计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 322-327.doi: 10.11896/j.issn.1002-137X.2019.06.049
所属专题： 人脸识别
崔景春, 王静
CUI Jing-chun, WANG Jing
摘要： 针对现有表情识别算法未考虑头部姿态及不能使用高像素图像的问题,提出一种基于随机森林算法的头部姿态估计(RF-HPE)网络与卷积神经网络相结合的模型。首先对输入图像作强度归一化,然后利用RF-HPE确定脸部标志关键点,从而确定脸部标志的位置,最后使用卷积神经网络提取特征并训练模型。该模型降低了光线强度对识别结果的影响,并且在不牺牲算法效率的情况下提高了训练精度。实验结果表明,所提出的改进模型的学习能力相比其他同类模型有较大优势,分类精度也显著提高。
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