Separation of overlapped fingerprint image using FinSNet and gender recognition from separated overlapped fingerprints using deep learning models.
Budget: ₹600 – ₹1,500 INR
Datasets to be taken as input for FinSNet machine learning model is Tsinguha overlapped latent fingerprint(TOLF database) and Tsinguha simulated overlapped fingerprint database(TSOF database).
Applying pre processing steps where ever required.
FinSNet model should give separated fingerprint images as output.
From these separated fingerprint images recognize gender using deep learning approach.
Code should be implemented in python.
For idea you can refer these research paper links.
1) "https://ieeexplore.ieee.org/document/9261449" -- FinSNet
2) "https://dl.acm.org/doi/10.1145/3400286.3418237" -- Gender Recognition
Objective is to improve accuracy that are got from these research papers.
You can prefer other gender recognition deep learning models also but should use FinSNet model for separation.
Applying pre processing steps where ever required.
FinSNet model should give separated fingerprint images as output.
From these separated fingerprint images recognize gender using deep learning approach.
Code should be implemented in python.
For idea you can refer these research paper links.
1) "https://ieeexplore.ieee.org/document/9261449" -- FinSNet
2) "https://dl.acm.org/doi/10.1145/3400286.3418237" -- Gender Recognition
Objective is to improve accuracy that are got from these research papers.
You can prefer other gender recognition deep learning models also but should use FinSNet model for separation.