3D-Aided Deep Pose-Invariant Face Recognition
Authors: Jian Zhao, Lin Xiong, Yu Cheng, Yi Cheng, Jianshu Li, Li Zhou, Yan Xu, Jayashree Karlekar, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks clearly demonstrate superiority of the proposed model over state-of-the-arts. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2National University of Defense Technology 3Panasonic R&D Center Singapore 4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 5Qihoo 360 AI Institute |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states the network is implemented using "publicly available TensorFlow" but does not provide concrete access (a link or explicit statement of release) to the authors' own source code for the methodology described. |
| Open Datasets | Yes | For qualitative evaluation, we show visualized results on Multi-PIE [Gross et al., 2010] benchmark dataset. For quantitative evaluation, we evaluate face recognition performance on Multi-PIE and IJB-A datasets. |
| Dataset Splits | Yes | The images with 11 poses within 90 and 20 illumination levels of the first 150 identities are used for training. For testing, one frontal view with neutral expression and illumination (i.e., ID07) is used as the gallery image for each of the remaining 100 identities, and other images are used as probes. For training and testing, 10 random splits are provided by each protocol, respectively. |
| Hardware Specification | Yes | trained using Adam (β1=0.5) on a single NVIDIA TITAN X GPU with 12G memory. |
| Software Dependencies | No | The proposed network is implemented based on the publicly available Tensor Flow [Abadi et al., ] platform. The paper mentions TensorFlow but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Throughout the experiments, the size of the RGB images of the input profile face (x), the simulator synthesis (x ), and the GLG prediction ( x) is fixed as 128 128; the sizes of the three RGB local patches (i.e., eyes, nose and mouth) are fixed as 80 40, 32 40, and 48 32, respectively; the dimensionality of the Gaussian random noise z is fixed as 100; the constraint factors λ0 to λ4 are empirically fixed as 0.05, 1.0, 0.1, 5 10 4, and 0.1, respectively; the batch size and learning rate are fixed as 16 and 3 10 5, respectively |