Fuad M. Alkoot
Identity Verification Using kNN Classifiers and Autistic Genetic Data
485 - 488
2017
11
8
International Journal of Biomedical and Biological Engineering
https://publications.waset.org/pdf/10007771
https://publications.waset.org/vol/128
World Academy of Science, Engineering and Technology
DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the knearest neighbor (kNN).
Open Science Index 128, 2017