UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition
Authors: qiufu li, Xi Jia, Jiancan Zhou, Linlin Shen, Jinming Duan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, Age DB, and Mega Face, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model Uni TSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as Cos Face, Arc Face, VPL, Anchor Face, and UNPG. |
| Researcher Affiliation | Collaboration | 1 National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China 2 Computer Vision Institute, Shenzhen University, China 3 School of Computer Science, University of Birmingham, UK 4 Aqara, Lumi United Technology Co., Ltd, China 5 Alan Turing Institute, UK 6 SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/CVI-SZU/Uni TSFace. |
| Open Datasets | Yes | We utilize four publicly available datasets for training, namely, CASIA-Web Face(33) (consisting of 0.5 million images of 10K identities), Glint360K(2) (comprising 17.1 million images of 360K identities), Web Face42M(35) (containing 42.5 million images of 2 million identities), and Web Face4M, which is a subset of Web Face42M with 4.2 million images of 0.2 million identities. |
| Dataset Splits | Yes | For the MFR Ongoing Challenge, the trained models are submitted to and evaluated by the online server. Specifically, we report 1:1 verification accuracy for LFW, CFP-FP, and Age DB. We report True Accept Rate (TAR) at False Accept Rate (FAR) levels of 1e-4 and 1e-5 for IJB-C. We report TARs at FAR=1e-4 for the Mask and Children test sets, and TARs at FAR=1e-6 for the GMR test sets. For the Mega Face Challenge 1, we report Rank1 accuracy for identification and TAR at FAR=1e-6 for verification. ... For example, when reporting the 1:1 verification accuracy on LFW, CFP-FP, Age DB, 10-fold validation is used. We first select the threshold that achieves the highest accuracy in the first 9 folds and then adopt this threshold to calculate the accuracy in the leave-out fold. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Pytorch" and "Retina Face" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We adopt customized Res Nets as our backbone following (7). We implement all models using Pytorch and train them using the SGD optimizer with a weight decay of 5e-4 and momentum of 0.9. For the face models on CASIA-Web Face, we train them over 28 epochs with a batch size of 512. The learning rate starts at 0.1 and is reduced by a factor of 10 at the 16th and 24th epoch. For both Glint360K and Web Face4M, we train the Res Nets for 20 epochs using a batch size of 1024. The learning rate is initially set at 0.1, while a polynomial decay strategy (power=2) is applied for the learning rate schedule. In the case of Web Face42M, we train the Res Nets for 20 epochs, using a larger batch size of 4096. The learning rate linearly warms up from 0 to 0.4 during the first epoch, followed by a polynomial decay (power=2) for the remaining 19 epochs. We include the detailed settings of all hyper-parameters used in Sec. 4 and Sec. 5 in the appendix for further reference. |