会议论文

Liu, Q.

英文

Advances in Intelligent Systems and Computing

2194-5357

2021

1274 AISC

456-464

10th International Conference on Computer Engineering and Networks, CENet 2020

16 October 2020 through 18 October 2020

Liu Q.Liu X.Shen T.Qiu X.

Springer Science and Business Media Deutschland GmbH

9789811584619

10.1007/978-981-15-8462-6_51

Acknowledgements. This work is supported by the National Natural Science Foundation of China (Nos. 61503128, 61772179), Hunan Provincial Natural Science Foundation of China (Nos. 2020JJ4152, 2019JJ40005), the Science and Technology Plan Project of Hunan Province (No. 2016TP1020), the General Scientific Research Fund of Hunan Provincial Education Department (Nos. 17C0223, 18A333), the Double First-Class University Project of Hunan Province (No. Xiangjiaotong [2018] 469), Postgraduate Research and Innovation Projects of Hunan Province (No. Xiangjiaotong [2019] 248–998), and Hengyang guided science and technology projects and Application-oriented Special Disciplines (No. Hengkefa [2018] 60-31).

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This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network (cGan). As a cGan model essentially, a method named pix2pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different gender...