Project Overview
The **Unique Iris Detection and Authentication Project** leverages advanced machine learning, spectral analysis, and image processing techniques for high-accuracy iris recognition. The system is designed to be robust, secure, and scalable, applicable in industries like banking, healthcare, and secure access control.
Key Features
- **Spectral Signature Analysis**: Extracts unique iris patterns across different spectral bands (e.g., near-infrared, visible).
- **Polarization Detection**: Detects light reflection anomalies for precise iris feature mapping.
- **CNN Machine Learning Models**: Uses deep learning to classify unique iris patterns.
- **Anomaly Detection & SSIM Matching**: Implements SSIM, Hamming distance, and contour analysis.
- **Modular Design**: Each component, from image preprocessing to anomaly detection, is flexible and extendable.
Installation & Usage
- Clone the repository: `git clone https://github.com/YourRepo/380K_Iris`
- Install dependencies: `pip install -r requirements.txt`
- Run preprocessing: `python src/iris_preprocessing.py`
- Train the model: `python src/iris_detection.py`
Authentication Methods
The system employs several methods for iris authentication, including Structural Similarity Index (SSIM), Hamming distance for binary iris codes, Gabor filtering, and CNN-based classification.
SSIM for Iris Comparison
SSIM quantifies the structural similarity between two iris images, comparing luminance, contrast, and structure:
$$ \text{SSIM}(x, y) = \frac{(2 \mu_x \mu_y + c_1)(2 \sigma_{xy} + c_2)}{(\mu_x^2 + \mu_y^2 + c_1)(\sigma_x^2 + \sigma_y^2 + c_2)} $$
Hamming Distance for Iris Authentication
The Hamming distance computes the difference between binary iris codes:
$$ d_{\text{Hamming}} = \frac{1}{N} \sum_{i=1}^{N} \text{XOR}(C_{\text{iris1}}(i), C_{\text{iris2}}(i)) $$
Convolutional Neural Networks (CNNs)
Our CNN model consists of convolutional, max-pooling, and fully connected layers, using softmax for binary classification:
$$ f_{\text{CNN}}(I_{\text{input}}) = \text{softmax}(W_{\text{dense}} \cdot \text{relu}(W_{\text{conv}} \cdot I_{\text{input}} + b_{\text{conv}}) + b_{\text{dense}}) $$
Protocol Overview
1. Image Preprocessing
The initial stage involves preprocessing the iris images to standardize and normalize them. Steps include normalization, resizing, and applying CLAHE for contrast enhancement:
```python def preprocess_image(image, target_size=(128, 128)): normalized_image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX) resized_image = cv2.resize(normalized_image, target_size) blurred_image = cv2.GaussianBlur(resized_image, (5, 5), 0) return blurred_image ```2. Spectral Signature Analysis
Spectral signature analysis examines the pixel intensity distribution across different bands, identifying key features in the iris pattern:
```python def compute_spectral_signature(image): channels = cv2.split(image) signature = {channel: cv2.calcHist([channels[i]], [0], None, [256], [0, 256]) for i, channel in enumerate(['R', 'G', 'B'])} return signature ```3. SSIM for Iris Comparison
SSIM quantifies the similarity between two iris images by comparing structural information, including luminance, contrast, and structure:
```python def compute_ssim(image1, image2): score, ssim_image = ssim(image1, image2, full=True) return score, (ssim_image * 255).astype(np.uint8) ```4. Hamming Distance for Iris Codes
Hamming distance computes the proportion of different bits between two binary iris codes:
```python def compute_hamming_distance(iris_code1, iris_code2): return np.sum(np.bitwise_xor(iris_code1, iris_code2)) / len(iris_code1) ```5. Machine Learning with CNNs
The CNN architecture extracts features through convolutional layers followed by max-pooling, flattening, and fully connected layers. A softmax activation function classifies the iris image:
```python def build_cnn(input_shape=(128, 128, 3), num_classes=2): model = Sequential([ Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), MaxPooling2D(pool_size=(2, 2)), Conv2D(64, (3, 3), activation='relu'), MaxPooling2D(pool_size=(2, 2)), Conv2D(128, (3, 3), activation='relu'), MaxPooling2D(pool_size=(2, 2)), Flatten(), Dense(128, activation='relu'), Dropout(0.5), Dense(num_classes, activation='softmax') ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model ```6. Anomaly Detection
Anomalies between iris images are detected by calculating the absolute difference and thresholding:
```python def detect_anomalies(image1, image2, threshold_value=50): difference_image = cv2.absdiff(image1, image2) _, anomaly_map = cv2.threshold(difference_image, threshold_value, 255, cv2.THRESH_BINARY) return anomaly_map ```Conclusion
This protocol integrates spectral analysis, SSIM, Hamming distance, and CNN models for highly accurate iris detection and authentication. The system is secure, scalable, and applicable to real-world biometric systems.
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