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Fusion of Dissimilar Features from Thermal Imaging for Improving Drunk Person Identification

Georgia Koukiou1,2 and Vassilis Anastassopoulos1
1.University of Patras, Electronics Laboratory (ELLAB), Department of Physics, Rio Patras, Greece
2.University of Thessaly, Department of Computer Science, Lamia, Greece
Abstract—In this work two different types of feature vectors are employed for drunk person identification. Both 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. For this purpose conventional data association techniques are employed to explore the available information. These data association techniques are gating, normalization and weighting. After that, fusion of the information is carried out using Support Vector Machines. 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.

Index Terms—drunk identification, feature fusion, thermal imaging, dissimilar features, SVMs

Cite: Georgia Koukiouand Vassilis Anastassopoulos, "Fusion of Dissimilar Features from Thermal Imaging for Improving Drunk Person Identification," International Journal of Signal Processing Systems, Vol. 5, No. 3, pp. 106-111, September 2017. doi: 10.18178/ijsps.5.3.106-111
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