Ridgeformer: Multi-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition

Accepted in IEEE International Conference on Image Processing 2025

GitHub |

Paper | PapersWithCode

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

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 and downloading SAM checkpoint
For more information, refer to SAM's official repository Link

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 [email protected]

spandey8
/

Ridgeformer