Facial Preprocessing Code in MATLAB

The algorithm performs facial illumination normalization by fusing logarithmic transformation results with AT algorithm output to generate uniformly lit preprocessed facial images. Implementation approach begins with logarithmic transformation to adjust intensity values in shadow and highlight regions, achieved using MATLAB's log() function on image pixel values. This step enhances dynamic range compression, balancing extreme lighting variations. The AT (Adaptive Thresholding) algorithm then further processes the transformed image through adaptive histogram equalization or similar techniques to optimize local contrast. Key functions involved include im2double() for image type conversion, log() for logarithmic operations, and adapthisteq() or custom AT implementation for adaptive thresholding. The fusion process typically employs weighted averaging or multiscale decomposition methods like Laplacian pyramid blending to combine both processed versions. This preprocessing stage creates optimized facial images with normalized illumination distribution, significantly improving suitability for downstream computer vision tasks such as face recognition, emotion detection, and biometric analysis. The complete pipeline can be implemented within 20-30 lines of MATLAB code using Image Processing Toolbox functions.