Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra. However, the development of HFR systems is challenging because of the significant domain gap between modalities and the lack of availability of large-scale paired multi-channel data. In this work, we leverage a pretrained face recognition model as a teacher network to learn domaininvariant network layers called Domain-Invariant Units (DIU) to reduce the domain gap. The proposed DIU can be trained effectively even with a limited amount of paired training data, in a contrastive distillation framework. This proposed approach has the potential to enhance pretrained models, making them more adaptable to a wider range of variations in data. We extensively evaluate our approach on multiple challenging benchmarks, demonstrating superior performance compared to state-of-the-art (SOTA) methods.
Overview
Training: The Domain Invariant Units (DIU) was trained on WebFace12M dataset (it contains over 12M images representing more than 600K identities).
Backbone: IResNet101
Parameters: 65.15M
Task: Heterogeneous Face Recognition Using Domain Invariant Units
Framework: Pytorch
Output structure: Batch of face embeddings (ie, features)
Evaluation of Models:
DIU
The proposed Domain Invariant Unit (DIU) framework. The lower layers of the student model are trained in contrastive framework to learn invariant features, while supervision from the distillation loss prevents overfitting.
Table1. Pola Thermal - Average Rank-1 Recognition Rate
Method
Mean (Std. Dev.)
DPM in
75.31% (-)
CpNN in
78.72% (-)
PLS in
53.05% (-)
LBPs + DoG in
36.8% (3.5)
ISV in
23.5% (1.1)
DSU (Best Result)
76.3% (2.1)
DSU-Resnet100
85.2% (5.8)
PDT
97.1% (1.3)
CAIM
95.0% (1.63)
DIU (Proposed)
97.8% (1.28)
Table 2. Experimental results on VIS-Thermal protocol of the Tufts Face dataset
Method
Rank-1
VR@FAR=1%
VR@FAR=0.1%
LightCNN
29.4
23.0
5.3
DVG
56.1
44.3
17.1
DVG-Face
75.7
68.5
36.5
DSU-Iresnet100
49.7
49.8
28.3
PDT
65.71
69.39
45.45
CAIM
73.07
76.81
46.94
DIU (Proposed)
82.94
85.9
74.95
Running Code
Minimal code to instantiate the model and perform inference:
bash
1$ cd bob.paper.icassp2024_diu_hfr
2$ conda create --name bob.paper.icassp2024_diu_hfr --file spec-file.txt
3$ conda activate bob.paper.icassp2024_diu_hfr # activate the environment4$ pip install pytorch-lightning==1.5.3
5$ buildout
6$ ./bin/bob bio pipelines vanilla-biometrics --help # test the installation
Please refer to the link for information about the License & Copyright terms and conditions.
Citation
If you find our work useful, please cite the following publication:
bibtex
1@inproceedings{george2024heterogeneous,
2 title={Heterogeneous face recognition using domain invariant units},
3 author={George, Anjith and Marcel, S{\'e}bastien},
4 booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
5 pages={4780--4784},
6 year={2024},
7 organization={IEEE}
8}
