Facial Sketching Based on Sub-Image Illumination Removal and Multiscale Edge Filtering

In this paper we introduce a method to automatically generate facial sketches from digital color photographs. First, the image is submitted to a sub-band homomorphic filtering for illumination compensation. Then, the result is fed into a multi-scale edge detection process. The homomorphic filter parameters and the optimal scales for the edge detector are acquired by means of a genetic algorithm optimization. An analysis comparing the results of the proposed method and those ones of a plain Canny edge detector is presented. The analysis has been performed considering manually labeled ground truth facial sketches and using three traditional evaluation metrics: Prattpsilas figure of merit (FoM), peak signal to noise ratio (PSNR) and structural similarity (SSIM). Among all metrics, the Prattpsilas FoM metric was the one with best stability. Parameter optimization using FoM rendered promising results to the proposed method. A subjective evaluation is also provided, which emphasizes the strengths of the proposed method.