Fingerprint Reconstruction using CNN

@glanegonsalves

Hey there Glane here, hope you're doing well. I can help you in image classification / segmentation task focusing on matching the masked output with the ground truth to get the dice/ jaccard score for segmentation and accuracy,precision,recall,f1 score for image classification, feel free to get in touch.

@prasanna2409

I propose to develop a deep learning–based image reconstruction model (such as a CNN or GAN architecture) that learns from the ground truth fingerprint images and accurately restores the masked regions in the input images. I will handle the full pipeline including data preprocessing, model design, training, evaluation, and result visualization. The solution will be implemented in Python (Anaconda) or MATLAB as required, with well-documented code, clear explanations of the model architecture, and performance reports to ensure easy verification and reproducibility.

@sulemanjaam

I will design and implement a deep learning–based image reconstruction model to accurately restore masked regions of fingerprint images. Using the provided ground truth and masked datasets, the model will learn ridge continuity and texture patterns to reconstruct missing fingerprint areas with high visual and structural similarity to the original images.

@lokeshv62

Hello, I can develop a high-quality fingerprint reconstruction model that accurately restores masked regions while preserving ridge continuity and fingerprint structure, using the dataset you provide. Approach I will treat this task as a deep learning–based image inpainting problem. Using the complete fingerprint images as ground truth, I’ll train a CNN / U-Net–style architecture to learn ridge flow, texture, and spacing, enabling realistic reconstruction of masked areas. The training will be mask-aware, ensuring the model focuses only on missing regions and does not alter unmasked fingerprint details. Implementation Python (Anaconda) using PyTorch or TensorFlow MATLAB support if required Image preprocessing and normalization Loss functions emphasizing ridge continuity and structural similarity (SSIM) Validation Side-by-side comparison with ground-truth images Quantitative evaluation (SSIM / MSE) Visual confirmation of clear, continuous ridges in reconstructed regions Deliverables Well-documented, reproducible source code Model architecture and training explanation Reconstruction results and usage instructions I understand the payment-after-verification requirement and will ensure the final solution meets accuracy and quality expectations before handoff. Best regards, Lokesh

@ravulap2

Hello, I’m interested in your fingerprint reconstruction project and I’m well-suited for this work. I completed my Master’s degree in the USA with a specialization in Artificial Intelligence / Image Processing, and I have hands-on experience building deep learning models for image reconstruction and analysis. I clearly understand your requirement: training a supervised model to reconstruct artificially masked fingerprint regions by learning ridge patterns from ground-truth images. This is a classic image inpainting / reconstruction problem, and I can implement it efficiently using Python (Anaconda) and/or MATLAB. Skills & Experience • Deep Learning & Neural Networks • Image Processing & Computer Vision • CNNs, Autoencoders, U-Net architectures • Python (Anaconda), MATLAB • Model evaluation (SSIM, PSNR) and visual validation Deliverables • Clean, well-documented code • Trained reconstruction model • Reconstructed fingerprint outputs • Performance evaluation and result explanation I’m comfortable with payment after full completion and verification. I can deliver accurate and visually continuous ridge reconstruction aligned closely with the ground truth. Looking forward to discussing the dataset and starting immediately. Pragnya