Accidental Face Recognition and Detection Using Machine Learning

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

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