Face sketch recognition based on edge enhancement via deep learning

Zhenzhu Xie; Fumeng Yang; Yuming Zhang; Congzhong Wu

doi:10.1117/12.2295758

15 November 2017 Face sketch recognition based on edge enhancement via deep learning

Zhenzhu Xie, Fumeng Yang, Yuming Zhang, Congzhong Wu

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053N (2017) https://doi.org/10.1117/12.2295758
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

Citation Download Citation

Zhenzhu Xie, Fumeng Yang, Yuming Zhang, and Congzhong Wu "Face sketch recognition based on edge enhancement via deep learning ", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053N (15 November 2017); https://doi.org/10.1117/12.2295758

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