MATLAB Implementation of Fingerprint Orientation Field

Fingerprint orientation field calculation is a critical preprocessing step that represents the directional characteristics of ridge patterns in local regions. Here's an analysis of five mainstream implementation methods with their algorithmic principles: Gradient-Based Method Utilizes pixel gradient information (x-direction and y-direction derivatives) to compute local orientation. The method works by sliding a window across the image and calculating the gradient direction histogram within each region, where the dominant direction corresponds to the ridge orientation. In MATLAB implementation, key functions include gradient() for derivative calculation and atan2() for direction computation. The advantage is high computational efficiency, though it's sensitive to noise. Structure Tensor Method Calculates the structure tensor matrix within image windows and determines the principal direction through eigenvector analysis. Compared to gradient method, this approach offers better stability for low-quality fingerprint images but requires slightly more computational resources. MATLAB implementation typically involves computing the tensor components using imgaussfilt() for smoothing and eig() for eigenvalue decomposition. Fourier Spectrum Method Performs Fourier transform on local regions, where the direction of concentrated spectral energy corresponds to ridge orientation. This method works well for fingerprint areas with clear periodicity but has limitations with non-uniform textures. Implementation in MATLAB uses fft2() for 2D Fourier transform and analyzes the spectral distribution through angle() function. Gabor Filter Bank Method Employs a bank of Gabor filters with different orientations to determine the optimal direction based on filter responses. This approach offers high accuracy and strong noise resistance but requires predefined filter parameters and has poorer real-time performance. MATLAB implementation involves creating Gabor filters using gabor() function and analyzing response magnitudes. Block Orientation Field Fitting Divides the image into blocks and applies least-squares fitting to estimate ridge directions. Suitable for global orientation field estimation, though it may lose detailed features and requires post-processing smoothing. Implementation in MATLAB typically uses blockproc() for image partitioning and polyfit() for direction fitting. Engineering Recommendations: Gradient and structure tensor methods are suitable for real-time systems, while Gabor filter method is more appropriate for high-precision offline scenarios. Practical applications often require combining orientation field smoothing and post-processing (such as low-pass filtering) to eliminate abrupt noise artifacts.