Drunk identification using far infrared imagery based on DCT features in DWT domain
Zhihua Xie, Peng Jiang, Ying Xiong, Ke Li · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Drunk driving problem is a serious threat to traffic safety. Automatic drunk driver identification is vital to improve the traffic safety. This paper copes with automatic drunk driver detection using far infrared thermal images by the holistic features. To improve the robustness of drunk driver detection, instead of traditional local pixels, a holistic feature extraction method is proposed to attain compact and discriminative features for infrared face drunk identification. Discrete cosine transform (DCT) in discrete wavelet transform (DWT) domain is used to extract the useful features in infrared face images for its high speed. Then, the first six DCT coefficients are retained for drunk classification by means of “Z” scanning. Finally, SVM is applied to classify the drunk person. Experimental results illustrate that the accuracy rate of proposed infrared face drunk identification can reach 98.5% with high computation efficiency, which can be applied in real drunk driver detection system.