Automated Fiducial Points Detection Using Human Body Segmentation

Accurately detected human body fiducial points provide an easy and efficient method for human body posture analysis and the extraction of anthropometric parameters. In the proposed work, an efficient algorithm for automated and accurate detection of fiducial points is developed for both the frontal and the lateral images. An algorithm for automatic human body segmentation of the frontal image is also developed using automatically detected set of primary fiducial points. Additional fiducial points are obtained by applying peak and valley algorithm on the silhouettes of each segment. The detection accuracy of the automatically detected fiducial points is calculated by comparing their locations with the manually marked fiducial points. The proposed algorithm is tested on 45 subjects including both male and female genders and variable Body Mass Indexes. In most cases, the algorithm successfully detects seventy fiducial points for each subject in the testing set. A quantitative analysis of the error in the position of the detected fiducial points shows that the algorithm performs better than the state-of-the-art algorithms found in the existing literature. In the evaluation of the algorithm, the percentage accuracy of the detected fiducial points is calculated and it is observed that the proposed algorithm performs better for the majority of the fiducial points.