Human Iris Detection

Implemented a CNN-based pupil localization pipeline in Python, reproducing the methodology from the paper “Accurate Eye Pupil Localization Using Heterogeneous CNN Models”.

Key Contributions:

• Model Implementation: Reproduced a heterogeneous CNN architecture for pupil detection, adapting the paper’s design to a practical Python/PyTorch workflow.
• Data Pipeline: Built preprocessing and augmentation routines to prepare eye image datasets for training and evaluation.
• Evaluation: Tracked train/test loss across training and validated predicted vs. ground-truth iris centers on held-out test images.

Methods Used:

• Dilation and Gaussian blurring for target (label) preprocessing
• Convolutional autoencoder as the model architecture
• Grid search across optimizers, loss functions, and activations to find the best combination:
  • optimizers: [SGD, Adam, Adamax, RMSprop]
  • loss functions: [MSE, MAE]
  • activations: [Tanh, ReLU, Sigmoid]
  • best combination found: Adam + MSE + Tanh

Software Used: OpenCV, PyTorch, NumPy, Pandas, scikit-learn

Sample Result:

These images show the heat map and predections generated by the model.