Open AccessA Biometric Recognition Method Using Deep CNN
Author(s) -
Vishalakshi Rituraj
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38951
Subject(s) - computer science , biometrics , convolutional neural network , artificial intelligence , pattern recognition (psychology) , facial recognition system , feature (linguistics) , face (sociological concept) , identification (biology) , transfer of learning , convolution (computer science) , feature extraction , machine learning , artificial neural network , social science , philosophy , linguistics , botany , sociology , biology
Face is perhaps the first biometric trait of a person that catches one’s eye and it remains in memory for a long due to its uniqueness created by almighty. Recognizing a person using his/her face, is very natural to us and we do not need any special training for identification. But computers are programmed for analyzing things and making predictions almost in similar fashion that our brain does. Then, the recognition takes place by using some techniques and trainings. The recognition system which uses biometric properties is itself a secure and trusted technique but use of neural networks make it highly accurate and add more worth to it. A CNN model works in a fully supervised or guided environment and performs all the tasks in a robotic manner. The convolutional layer which lies in CNN model performs the complex calculation and extracts all the unique and useful features without any human involvement. I preferred to adopt Transfer learning in my work, by importing a pre-trained CNN model and I found 97.5% accuracy in recognition when I tested the model with my test samples. Keywords: Biometrics, Convolution, AlexNet, Feature Extraction, Transfer Learning