Detection of alcohol intoxication via online handwritten signature verification

The Internet has recently facilitated various methods of personal authentication based on biometrics. Personal authentication techniques based on human physical features include fingerprint matching, vein matching, facial recognition, speech recognition, and signature recognition. In this study, we investigate the detection of alcohol intoxication on the basis of handwritten signatures. As in the case of previous related studies, we found that the signature attestation rate varies at an individual level according to sex, age, acetaldehyde removal efficiency, and individual constitution.In this study, we employed 30 people to evaluate the change in a handwritten signature before and after alcoholic intake. First, we measured the signature verification rate using the online signature verification system. The signature verification rate measured using the WACOM Tablet pen before alcohol consumption was 97.0%. Moreover, the size of the characters and the interval between the characters were measured for signatures collected after alcohol consumption. We detected the level of alcohol intoxication on the basis of the total time taken for writing the signature, the average pressure of the brush, the two-dimensional writing speed, and internal angle of stroke turns. The maximum alcohol detection rate of this method was 95.1%, which was achieved when the examinees were tested 35 min after alcohol consumption. The rate of alcohol detection increases with the alcohol density in an examinee’s breath.

• This paper presents a novel algorithm for the detection of alcohol intoxication via online handwritten signature verification.
• We used a signature verification system based on dynamic positional warping of the x, y coordinates.
• An alcohol detection rate exceeding 90 percent was possible using the fuzzy logic algorithm.
• The alcohol detection rate was 95.1% when measured 35 min after consumption.information.