Deep Graphical Feature Learning for Face Sketch Synthesis
Authors: Mingrui Zhu, Nannan Wang, Xinbo Gao, Jie Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public face sketch databases show that our method outperforms state-of-the-art methods, in terms of both synthesis quality and recognition ability. In this section, we report the experimental results of the proposed DGFL method quantitatively and qualitatively. We conducted our experiments on the Chinese University of Hong Kong (CUHK) face sketch database (CUFS) [Tang and Wang, 2009]. |
| Researcher Affiliation | Academia | Mingrui Zhu , Nannan Wang , Xinbo Gao , Jie Li State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University, Xi an 710071, China State Key Laboratory of Integrated Services Networks, School of Telecommunications, Xidian University, Xi an 710071, China mrz.edu@gmail.com, nnwang@xidian.edu.cn, {leejie,xbgao}@mail.xidian.edu.cn |
| Pseudocode | Yes | Algorithm 1 Deep Graphical Feature Learning for Face Sketch Synthesis |
| Open Source Code | No | The paper states that |
| Open Datasets | Yes | We conducted our experiments on the Chinese University of Hong Kong (CUHK) face sketch database (CUFS) [Tang and Wang, 2009]. The CUFS database consists of face photos from three databases: the CUHK student database [Tang and Wang, 2002] (188 persons), the AR database [Martinez and Benavente, 1998] (123 persons) and the XM2VTS database [K. Messer and J. Luettin, 1999] (295 persons). |
| Dataset Splits | Yes | For the CUHK student database, 88 pairs of face photo-sketch are taken for training and the rest for testing. For the AR database, we randomly choose 80 pairs for training and the rest 43 pairs for testing. For the XM2VTS database, we randomly choose 100 pairs for training and the rest 195 pairs for testing. |
| Hardware Specification | Yes | All experiments are conducted using Python on Ubuntu 14.04 system with i7-4790 3.6G CPU and 12G NVIDIA Titan X GPU. |
| Software Dependencies | No | The paper mentions "Python on Ubuntu 14.04 system" but does not specify exact version numbers for Python itself or any other relevant software libraries (e.g., TensorFlow, PyTorch, NumPy) used for the implementation. |
| Experiment Setup | Yes | The parameters used were set as follows: the image patch size was 10, the overlap size was 5, the size of search region was 5, the number of candidate patches K was set to 10, the α was set to 0.25, the λ was set to 2. |