Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Authors: Bing Cao, Nannan Wang, Xinbo Gao, Jie Li, Zhifeng Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks, comparing with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an, China 2School of Electronic Engineering, Xidian University, Xi an, China 3School of Telecommunications Engineering, Xidian University, Xi an, China 4Tencent AI Lab, Shenzhen, China bcao@stu.xidian.edu.cn, nnwang@xidian.edu.cn, {leejie,xbgao}@mail.xidian.edu.cn, michaelzfli@tencent.com |
| Pseudocode | Yes | Algorithm 1 Multi-Margin Decorrelation for Heterogeneous Face Recognition |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The CASIA NIR-VIS 2.0 Face Database is the most challenging and the largest NIR-VIS database... Oulu-CASIA NIR-VIS Database consists of 80 subjects... |
| Dataset Splits | Yes | We follow the partition protocols in [He et al., 2018] and evaluate the proposed method on this database with 10-fold experiments. In the training phase, there are about 6100 NIR face images and 2500 VIS face images share 360 identities in each protocol. In the testing phase, there are about 6100 NIR face images in the probe set and 358 VIS face images in the gallery set. The similarity matrix of probe set and gallery set is 6100 358, computed by cosine distance. |
| Hardware Specification | Yes | We implement all the experiments in this paper by Py Torch under the environment of Python 3.7 on Ubuntu 16.04 system with i7-6700K CPU and NVIDIA TITAN Xp GPU. |
| Software Dependencies | No | The paper mentions "Py Torch under the environment of Python 3.7 on Ubuntu 16.04 system". While Python 3.7 and Ubuntu 16.04 have versions, the version of PyTorch (a key library) is not specified. |
| Experiment Setup | Yes | We set the initial learning rate to 1e 4, which is gradually reduced to 1e 6. The batch size to 16 and the trade-off parameter λN, λV , λ1 and λ2 are set to 0.6, 0.4, 10 and 1 respectively. The angular margin m is set to 0.9 in this paper. |