超分辨率重建的素描人脸识别

目的 基于学习的超分辨率重建由于引入了先验知识,可以更好地描述图像的细节部分,显著地增强图像的分辨率,改善图像的视觉效果。将超分辨率重建应用在素描人脸识别中,既可以增加人脸图像的质量也可以有效地提高识别精度。方法 首先利用特征脸算法根据素描图像合成人脸灰度图像,然后对合成的人脸图像利用稀疏表示进行超分辨率重建,最后利用主成分分析对重建前后的合成人脸分别进行识别。结果 在香港中文大学的素描人脸库(CUFS)上进行实验。经过超分辨率重建之后的人脸在眼睛等部位细节描述更好。同时,由于重建过程中引入了先验知识,重建之后的素描人脸识别率有提高。支持向量机算法得到的识别率由重建前的65%提高至66%,本文利用的主成分分析算法得到的识别率由重建前的87%提高至89%。结论 基于超分辨率重建的素描人脸识别算法可以有效地改善合成人脸图像的视觉效果并且提高素描人脸识别精度。

Sketch face recognition based on super-resolution reconstruction

Objective Super-resolution reconstruction by learning can better describe image details and significantly enhance image resolution, thus improving the visual effect of the image because of the introduction of priori knowledge. Applying super-resolution reconstruction to sketch face recognition does not only improve the quality of the image but also effectively increases the recogniton rate.Method First, eigenface algorithm is used to synthesize a photo according to the input sketch. Then, super-resolution reconstruction via sparse representation is executed on the synthesized photo. Finally, principal component analysis is employed to recognize the synthesized photos that have been formed before the reconstruction and after. The experiment is performed on CUHK Face Sketch Database (CUFS). Result Experimental results show that, after super-resolution reconstruction, the synthesized photo can describe the facial details better, such as the eyes. Moreover, because of the introduction of priori knowledge, the sketch face recognition rate is improved after reconstruction. Experimental results also indicate that the recognition rate of support vector machine algorithm is improved from 65% to 66%, and the recognition rate of the principal component analysis algorithm is improved from 87% to 89%. Conclusion Sketch face recognition based on super-resolution reconstruction can improve the image visual effect and increase the sketch face recognition rate effectively.