| --- |
| license: cc-by-nc-4.0 |
| base_model: spandey8/Ridgeformer |
| tags: |
| - Image Classification |
| - Fingerprint Matching |
| - Biometrics |
| - Cross Domain Fingerprint Recognition |
| - Image Feature Extraction |
| - Multi Stage Training Strategy |
| - Contrastive Learning |
| - Keypoint Detection |
| libraries: |
| - PyTorch |
| - Transformers |
| - NumPy |
| language: |
| - en |
| --- |
| # Ridgeformer: Multi-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition |
| ### Accepted in IEEE International Conference on Image Processing 2025 |
|
|
| <a href="https://github.com/KNITPhoenix/Ridgeformer"> |
| <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> |
| <span class="link-text" style=" margin-right: 5px;">GitHub</span> |
| </a> | |
| <a href="https://arxiv.org/abs/2506.01806"> |
| <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> |
| <span class="link-text" style="margin-right: 5px;">Paper</span> |
| </a> | |
| <a href="https://paperswithcode.com/paper/ridgeformer-mutli-stage-contrastive-training"> |
| <svg xmlns="http://www.w3.org/2000/svg" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"> |
| <span class="link-text" style="margin-right: 5px;">PapersWithCode</span></svg> |
| </a> |
|
|
|  |
|
|
| ## Installations and environment creation |
| - conda create -n ridgeformer python=3.8.19 |
| - conda activate ridgeformer |
| - pip install -r requirements.txt |
|
|
| It requires timm version 0.5.0 and can be installed with the given .whl file. |
| We used pytorch>=2.2.2 for CUDA=12.2 |
|
|
| ## Preparing data |
|
|
| ### Datasets used in training and their application link |
| - [Ridgebase(RB)](https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html#title_0) |
| - [The Hong Kong Polytechnic University Contactless 2D to Contact-based 2D Fingerprint Images Database Version 1.0](http://www4.comp.polyu.edu.hk/~csajaykr/fingerprint.htm) |
| - [IIITD SmartPhone Fingerphoto Database V1 (ISPFDv1)](https://iab-rubric.org/index.php/ispfdv1) |
| - [IIITD SmartPhone Finger-Selfie Database V2 (ISPFDv2)](https://iab-rubric.org/index.php/ispfdv1) |
|
|
| ### Testing dataset: |
| - The Hong Kong Polytechnic University Contactless 2D to Contact-based 2D Fingerprint Images Database Version 1.0 (HKPolyU) |
| - Ridgebase (RB) |
|
|
| ### Preprocessing ISPFD v1 and v2 datasets |
| - scipts in ISPFD_preprocessing directory are used to segment out contactless images in ISPFD dataset |
| - requires SAM checkpoint and openai clip |
| - can be used after installing [segment-anything](https://pypi.org/project/segment-anything/) and downloading SAM [checkpoint](https://github.com/facebookresearch/segment-anything#model-checkpoints) |
| - For more information, refer to SAM's official repository [Link](https://github.com/facebookresearch/segment-anything) |
| |
| ### Manifest files creation for dataloaders |
| - script data_folder_creation.py in datasets directory is used to arrange the datasets in a specific folder structure |
| ``` |
| Subject ---------> |
| finger ----------> |
| background and instances |
| ``` |
| - script manifest_file_creation.py in datasets directory is used to create manifest files used in dataloaders. The manifest file structure will be as follows: |
| |
| ``` |
| { |
| Unique_finger_id_1:{ |
| 'Contactless': ( list of paths of all contactless images ) |
| 'Contactbased': ( list of paths of all contactbased images ) |
| }, |
| Unique_finger_id_2:{ |
| 'Contactless': ( list of paths of all contactless images ) |
| 'Contactbased': ( list of paths of all contactbased images ) |
| } |
| ......} |
| ``` |
| |
| ## Training |
| Stage 1 - train_combined.py is used to train the model on Stage 1 of our architecture |
|
|
| Stage 2 - train_combined_fusion.py is used to train the model on Stage 2 of our architecture |
|
|
| All the performance ROCs and matrices are saved in combined_models_scores directory |
|
|
| All tensorboard logs are saved in experiment_logs directory |
| |
| ## Testing and Evaluation |
| ### HKPolyU |
| - Evaluation of HKPolyU testing dataset on finetuned checkpoint from Stage 1 can be done using hkpoly_evaluation_phase1.py |
| - Evaluation of HKPolyU testing dataset on finetuned checkpoint from Stage 2 can be done using hkpoly_evaluation_phase2.py |
| |
| ### Ridgebase |
| - Evaluation of ridgebase testing dataset on pretrained checkpoint from Stage 1 can be done using rb_evaluation_phase1.py |
| - Evaluation of ridgebase testing dataset on pretrained checkpoint from Stage 2 can be done using rb_evaluation_phase1.py |
| |
| ## Performance compared with SOTA methods on HKPolyU dataset (1:1 verification) |
| |Method | Probe | Gallery | EER(%) | TAR(%)@FAR=.01 | |
| | :---: | :---: | :---: | :---: | :---: | |
| |Verifinger | CL | CB | 19.31 | 76.00 | |
| |RTPS+DCM | CL | CB | 14.33 | 50.50 | |
| |Multi-Siamese | CL | CB | 7.93 | 54.00 | |
| |MANet | CL | CB | 4.13 | 88.50 | |
| |ML Fusion | CL | CB | 4.07 | 94.40| |
| |Ridgeformer (Ours)| CL | CB | 2.83 | 89.34| |
| |
| ## Performance compared with SOTA methods on HKPolyU dataset (1:N identification) |
| |Method | Probe | Gallery | R@1 | R@10 | |
| | :---: | :---: | :---: | :---: | :---: | |
| |Verifinger | CL | CB | 80.73 | 91.00 | |
| |RTPS+DCM | CL | CB | 66.67 | 83.00 | |
| |Multi-Siamese | CL | CB | 64.59 | 91.00 | |
| |MANet | CL | CB | 83.54 | 97.00 | |
| |Ridgeformer (Ours)| CL | CB | 87.40 | 98.23 | |
| |
| ## Performance compared with SOTA methods on Ridgebase dataset (1:1 verification) |
| |Method | Probe | Gallery | EER(%) | TAR(%)@FAR=.01 | |
| | :---: | :---: | :---: | :---: | :---: | |
| |Verifinger | CL | CB | 18.90 | 57.60 | |
| |Ridgeformer (Ours) | CL | CB | 5.25 | 82.23 | |
| |AdaCos(CNN) | CL | CL | 21.30 | 61.20 | |
| |Verifinger | CL | CL | 19.70 | 63.30 | |
| |Ridgeformer (Ours)| CL | CL | 7.60 | 85.14 | |
| |
| ## Performance compared with SOTA methods on Ridgebase dataset (1:N identification) |
| |Method | Probe | Gallery | R@1 | R@10 | |
| | :---: | :---: | :---: | :---: | :---: | |
| |Verifinger | CL | CB | 72.50 | 89.20 | |
| |Ridgeformer (Ours) | CL | CB | 69.90 | 92.64 | |
| |Verifinger | CL | CL | 85.20 | 91.40 | |
| |AdaCos(CNN) | CL | CL | 81.90 | 89.50 | |
| |Ridgeformer (Ours)| CL | CL | 100.00 | 100.00 | |
| |
| ## Contact |
| For more information or any questions, feel free to reach us at spandey8@buffalo.edu |
