A Comparative Analysis of RootSIFT and SIFT Methods for Drowsy Features Extraction

摘要

Drowsiness during driving is a very serious concern in the society. Thousands of human lives are affected due to drowsy driving. So a system that can detect drowsy drivers is necessary in this situation. This paper does a comparative analysis of the approaches called Scale Invariant Feature Transform(SIFT) and RootSIFT for drowsy features extraction. RootSIFT is an enhanced SIFT descriptor. SIFT has been the widely used technique for feature extraction due to its invariance to scale, rotation, illumination, viewpoint and translations. So the enhancement to SIFT to detect drowsy features has made an outcome more likely. The enhanced SIFT called RootSIFT achieves 93.55% accuracy, which is better than normal SIFT in extracting the drowsy features.