A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers

Abstract

In modern culture, the rise in consumption of alcohol has caused many issues and its potentially adverse effects on human health are a well-known reality. The image of Iris helps to diagnose the alcohol drinkers efficiently. The prevailing methodology can take up plenty of time to execute the process, and may also produce less accuracy. This paper has proposed an effective method for the classification of alcohol drinkers as well as the identification of iris damaged levels utilizing MDLNN. Initially, the Log Gabor (LG), HOG feature, GLAC feature, LGXP, alongside canny edge detection (CED), features are extracted as of the alcohol drinker’s image. Next, the extracted features are selected utilizing BFO. Next, the image is classified as a more, medium, or less drunk with the help of MDLNN. Later on, the AHE is used to enhance the contrast of the drunken person’s iris image. Then, the image’s foreground is enhanced by utilizing HGBFDWT. Thereafter, segmentation is performed based upon the mask value utilizing OTMO. Finally, find what proportion (percentage) the iris is damaged based upon the Euclidean distance betwixt the original iris image of the drunk person and the segmented damaged level of the iris image. In an experimental assessment, the proposed work attains better accuracy than the prevailing methodologies.

Keywords

• Bacterial Foraging Optimization (BFO)
• Modified Deep Learning Neural Network (MDLNN)
• Adaptive Histogram Equalization (AHE)
• Hybridization of Gaussian bandpass filter and Discrete Wavelet Transform (HGBFDWT)
• Otsu Thresholding and Morphological Operation (OTMO)