Iris Recognition Image Normalization Processing

As a significant branch of biometric technology, iris recognition requires normalization of the captured annular iris region as one of its core steps. Due to the inherent circular characteristics of the iris and variations in size, position, and angle of iris images under different individuals or acquisition conditions, direct feature extraction faces matching difficulties. Therefore, converting the iris region from its annular structure in the original image to a uniformly sized rectangular image becomes a critical preprocessing step in the iris recognition pipeline. Implementation-wise, this involves coordinate transformation algorithms that can be coded using image processing libraries like OpenCV or MATLAB's Image Processing Toolbox.

The core of the normalization process lies in polar coordinate transformation. The iris region typically appears as an annulus in the original image, with boundaries defined by both the inner circle (pupil boundary) and outer circle (iris outer boundary). Through polar coordinate transformation, the annular iris region is "unwrapped" into a fixed-size rectangular image. Specifically, this process uses the pupil center as the origin, mapping each point in the annular region through radial and angular sampling to pixel values in the rectangular image. The radial direction corresponds to the rectangle's width (typically normalized to a fixed number of pixels from inner to outer circle), while the angular direction corresponds to the rectangle's height (covering 0 to 360 degrees). In code implementation, this can be achieved using functions like cv2.remap() in OpenCV or creating custom mapping functions that calculate the radial and angular coordinates.

This normalization offers multiple advantages: First, it eliminates scale, translation, and rotation differences caused by capturing distance, lighting conditions, or eye rotation, providing uniform input for subsequent feature extraction. Second, the rectangular structure facilitates the application of traditional image processing algorithms or deep learning models for feature encoding. Finally, the normalized image preserves iris texture features such as folds and spots - crucial information for high-accuracy identification. From a programming perspective, this enables easier implementation of feature extraction algorithms like Gabor filters or convolutional neural networks (CNNs).

Notably, normalization quality highly depends on accurate localization of iris inner and outer boundaries. If boundary detection deviates, it may cause distortion or information loss in the normalized image. Therefore, in practical applications, normalization algorithms typically need to be combined with efficient edge detection and fitting algorithms to ensure polar coordinate transformation accuracy. Common implementations include using Hough circle transform or Daugman's integro-differential operator for boundary detection before normalization.

The normalized iris image not only provides standardized input for feature extraction but also establishes interoperability foundations for cross-device, cross-scenario iris recognition systems, further promoting widespread application of iris recognition technology in security authentication, financial payment, and other fields. The entire process can be implemented as a modular pipeline with functions handling boundary detection, coordinate transformation, and image interpolation separately.