01489nas a2200241   4500000000100000000000100001008004100002260001200043653002900055653001800084653003800102653002700140653002200167100001400189700002000203700001700223245006500240856009600305300001000401490000600411520081600417022001401233       2019                            d  c03/201910aDriver Fatigue Detection10aEye Detection10aScale Invariant Feature Transform10aSupport Vector Machine10aTraffic Accidents1 aSaima Naz1 aSheikh Ziauddin1 aAhmad Shahid00aDriver Fatigue Detection using Mean Intensity, SVM, and SIFT  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/10/ijimai_5_4_10_pdf_16089.pdf  a86-930 v53 aDriver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes.  a1989-1660