A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique

TL;DR

This paper introduces a robust face detection method based on a color probabilistic estimation technique that accurately detects skin regions across diverse skin tones with low noise sensitivity and computational efficiency.

Contribution

It proposes a novel two-stage approach using Gaussian modeling and optimal thresholding for skin detection, improving accuracy and adaptability over existing methods.

Findings

Achieved 99.25% accuracy on FEI database

Effective across all skin types with training-based adaptation

Low noise sensitivity and computational complexity

Abstract

Human face perception is currently an active research area in the computer vision community. Skin detection is one of the most important and primary stages for this purpose. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, HSI or YCBCR. Results show that these methods cannot provide an accurate approach for every kind of skin. In this paper, an approach is proposed to solve this problem using a color probabilistic estimation technique. This approach is including two stages. In the first one, the skin intensity distribution is estimated using some train photos of pure skin, and at the second stage, the skin pixels are detected using Gaussian model and optimal threshold tuning. Then from the skin region facial features have been extracted to get the face from the skin region. In the results section, the proposed approach is applied on FEI database and the accuracy rate reached 99.25%. The proposed approach can be used for all kinds of skin using train stage which is the main advantage among the other advantages, such as Low noise sensitivity and low computational complexity.