Fusion using neural networks for intoxication identification

Abstract

Fusion of dissimilar features by means of neural networks is demonstrated in this work aiming at improving the performance of these features for drunk person identification. The features are coming from the thermal images of the face of the inspected persons and have been derived using different image analysis techniques. Thus, they convey dissimilar information, which has to be transferred onto the same framework and fused to result into a decision with improved reliability. Conventional data association techniques are employed to explore the available information. After that, fusion of the information is carried out using Neural Networks. The resulting decision is of higher reliability compared to those achieved using the individual features separately. Experimental results are provided based on an existing sober-drunk database. The main advantage of the method is that it is not invasive and all the information is acquired remotely. In practice, an electronic system incorporating the proposed approach will point out to the police to whom an extended inspection for alcohol consumption is due.