Computer Based Detection of Alcohol Consumed Candidates Using Face Expressions with SIFT and Bag of Words

Bekkanti, Ashok; Parasa, Gayatri; Krishna, Azmira; Karimunnisa, Syed; Basha, Cmak Zeelan

doi:10.1109/icoei51242.2021.9453057

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Bekkanti et al [11] concocted a computer-based detector to distinguish between drunk and sober individuals using human emotions. To perform regression computation on data gathered from human sentiments, this study uses a multilayered perceptron-based back propagation neural network (MLP-BBN), a convolutional neural network (CNN), an adaptive neuro-fuzzy inference system (ANFIS), a simple vector machine, and a probabilistic neural network.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

Kumar¹,

Kumar²,

Singh³

et al. 2022

Scientific Programming

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Although the statistics show a slow decline in traffic accidents in many countries over the last few years, drunk or drug-influenced driving still contributes to enough shares in those records to act. Nowadays, breath analysers are used to estimate breath alcohol content (BAC) by law enforcement as a preliminary alcohol screening in many countries. Therefore, since breath analysers or field sobriety testers do not accurately measure BAC, the analysis of blood samples of individuals is required for further action. Many researchers have presented various approaches to detect drunk driving, for example, using sensors, face recognition, and a driver’s behaviour to confound the shortcomings of the time-honoured approach using breath analysers. But each one has some limitations. This study proposed a plan to distinguish between drivers’ states, that is, sober or drunk, by the use of transfer learning from the convolutional neural network (CNN) features to the random forest (RF) features with an accuracy of up to 93%, which is higher than that of existing models. With the same dataset, to validate our research, a comparative analysis was performed with other existing model classifiers such as the simple vector machine (SVM) with an accuracy of 65% and the K-nearest neighbour (KNN) with an accuracy of 62%, and it was found that our approach is an optimized approach in terms of accuracy, precision, recall, F1-score, AUC-ROC curve, and Matthew’s correlation coefficient (MCC) with confusion matrix.

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Section: Related Workmentioning

confidence: 99%