Quantum Iris Authentication

The refined circle detection now focuses more accurately on specific features of the iris, such as:

• Crypts: Detected with smaller circles near the colarette.
• Radial Streaks: Circles align with the angular patterns radiating outward.
• Contraction Furrows: Identified near the periphery of the iris.
• Iris Freckles and Wolfflin Nodules: Highlighted by distinct circle clusters.

Next Steps:

1. Implement quantum-inspired feature extraction to analyze and encode these detected features.
2. Model light interaction using a quantum field approach to simulate how iris features respond to varying light conditions.
3. Develop a recognition model capable of matching iris features across different dilation and constriction states.
4. Integrate quantum-safe encryption to securely store the iris feature data.

​

Next, we will develop a quantum-inspired feature extraction model that analyzes the detected iris features and encodes them using quantum state representations. This model will:

1. Extract Key Iris Features: Quantify features such as crypts, radial streaks, contraction furrows, and nodules.
2. Apply Quantum Feature Mapping: Use quantum-inspired equations to transform these features into a multidimensional Hilbert space.
3. Implement Recognition Algorithms: Develop a model to match iris features under different dilation states.
4. Introduce Quantum-Safe Encryption: Securely store the extracted feature vectors for biometric identification.

# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# Sample data for circles_refined (replace with your actual data)
circles_refined = np.random.rand(50, 3) * 100

# Extract the coordinates and radii of the detected circles as features
if circles_refined is not None:
    iris_features = np.array([[x, y, r] for (x, y, r) in circles_refined])
else:
    iris_features = np.empty((0, 3))

# Standardize the features
scaler = StandardScaler()
standardized_features = scaler.fit_transform(iris_features)

# Apply Quantum-inspired Feature Mapping using PCA
pca = PCA(n_components=3)
quantum_mapped_features = pca.fit_transform(standardized_features)

# Visualization setup
fig = plt.figure(figsize=(8, 6), facecolor='black')
ax = fig.add_subplot(111, projection='3d', facecolor='black')

# Scatter plot with midnight theme colors
sc = ax.scatter(
    quantum_mapped_features[:, 0],
    quantum_mapped_features[:, 1],
    quantum_mapped_features[:, 2],
    c=quantum_mapped_features[:, 2],
    cmap='plasma',
    edgecolor='none',
    alpha=0.9
)

# Customizing plot appearance
ax.set_title('Quantum-Inspired Feature Mapping', color='white')
ax.set_xlabel('Quantum Axis 1', color='white')
ax.set_ylabel('Quantum Axis 2', color='white')
ax.set_zlabel('Quantum Axis 3', color='white')
plt.show()