Driving Alert System through feature extraction on facial images using neural network classifier

Abstract

Any person driving for a long period of time without taking rest causes them to be weary. This driving alert system alerts the driver from causing accidents. It uses Radial Basis Function neural network (RBFNN) for classifying the drivers’ facial expression. This approach extracts the features from facial characteristics points (FCP) of face images. Nearly,19 FCP are selected from the face image at variable lighting condition .The state of the eye is detected by processing frontal or side views of the face image taken by a camera mounted in the car and then automatically trained using a cross correlation based optical flow method. Forty eight features are extracted from the FCP based on openness and closeness of eye, width of mouth. The input features are then categorized into normal, anger and drowsiness expression. The performance is evaluated in terms of recognition rate of RBFNN classifier.

Authors and Affiliations

V. Ezhilarasi, N. Srinivasan, M. Kavitha

Keywords

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  • EP ID EP22955
  • DOI -
  • Views 224
  • Downloads 3

How To Cite

V. Ezhilarasi, N. Srinivasan, M. Kavitha (2016). Driving Alert System through feature extraction on facial images using neural network classifier. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 4(12), -. https://europub.co.uk/articles/-A-22955