This project focuses on developing a palmprint recognition system using a self-supervised learning approach, inspired by SimCLR, to address the common challenge of limited labeled data in biometric identification. The system leverages contrastive learning with a ResNet-18 backbone, comprehensive image preprocessing with OpenCV, and a Streamlit UI for real-time demonstration.

Kaggle Notebook: Palmprint Recognition for Authentication

Key Contributions & Impact

• Self-Supervised Learning Implementation: Developed a palmprint matching system using a self-supervised learning approach inspired by SimCLR. This approach effectively addressed the challenge of limited labeled data, a common issue in biometric identification tasks.
• Contrastive Learning with ResNet-18: Utilized contrastive learning principles with a ResNet-18 backbone, pre-trained on ImageNet and fine-tuned using the NT-Xent loss function. This architecture enabled the model to learn robust representations of palmprint features.
• Image Preprocessing with OpenCV: Implemented comprehensive image preprocessing techniques using OpenCV to enhance the quality and consistency of the palmprint images, improving the model's accuracy and robustness.
• Streamlit UI Development: Created a user-friendly Streamlit UI to provide a real-time demonstration of the system's ability to match palmprints. This allows for easy testing and visualization of the system's performance.
• Achieved Promising Matching Results: The system demonstrated promising results in accurately matching new palmprints to existing ones from the same individual, showcasing the effectiveness of the self-supervised learning approach.

Challenges & Solutions

• Choosing Appropriate Augmentations: Selecting the right data augmentations for contrastive learning is crucial for performance. Solution: Experimented with a range of image augmentations (e.g., rotations, crops, color jittering) and carefully selected a combination that promoted the learning of invariant features while preserving essential palmprint details. Referenced the SimCLR paper and related research for best practices.

Lessons Learned

• Effectiveness of Self-Supervised Learning: Gained practical experience with self-supervised learning and its ability to address the challenge of limited labeled data in computer vision tasks.
• Importance of Image Preprocessing: Reinforced the critical role of image preprocessing in improving the performance and robustness of computer vision models.
• Contrastive Learning Principles: Deepened my understanding of contrastive learning and its application in learning robust feature representations.
• Biometric Identification Challenges: Gained insights into the specific challenges and considerations involved in developing biometric identification systems.