【摘要】 提出了一种基于中心对称四重模式的光照不变度量(CSQPIM),以解决严重光照变化人脸识别问题.首先,将对数人脸局部区域中中心对称四重模式(CSQP)的像素对之差定义为CSQPIM单元,CSQPIM单元的值可能为正或负.CSQPIM模型由正负CSQPIM单元合成得到.然后,通过控制正负CSQPIM单元的比例,CSQPIM模型可以生成多张CSQPIM图像.单张CSQPIM图像与饱和函数可以形成CSQPIM-face.多张CSQPIM图像采用扩展的稀疏表示分类(ESRC)作为分类器,从而形成基于CSQPIM图像的分类(CSQPIMC).进一步,CSQPIM模型与预先训练的深度学习(PDL)模型集成,以构建CSQPIM-PDL模型.最后,在Extended Yale B,CMU PIE和Driver人脸数据库上的实验结果表明,所提出的方法对剧烈光照变化非常有效.更多还原

【Abstract】 A centre symmetric quadruple pattern-based illumination invariant measure(CSQPIM) is proposed to tackle severe illumination variation face recognition. First, the subtraction of the pixel pairs of the centre symmetric quadruple pattern(CSQP) is defined as the CSQPIM unit in the logarithm face local region, which may be positive or negative. The CSQPIM model is obtained by combining the positive and negative CSQPIM units. Then, the CSQPIM model can be used to generate several CSQPIM images by controlling the proportions of positive and negative CSQPIM units. The single CSQPIM image with the saturation function can be used to develop the CSQPIM-face. Multi CSQPIM images employ the extended sparse representation classification(ESRC) as the classifier, which can create the CSQPIM image-based classification(CSQPIMC). Furthermore, the CSQPIM model is integrated with the pre-trained deep learning(PDL) model to construct the CSQPIM-PDL model. Finally, the experimental results on the Extended Yale B, CMU PIE and Driver face databases indicate that the proposed methods are efficient for tackling severe illumination variations.更多还原