Open AccessPharmacy Impact for Distinguishing Normal Face from Abnormal Face Due to COVID- 19
Author(s) -
Payal Bose,
Shawni Dutta,
Vishal Goyal,
Samir Kumar Bandyopadhyay
Publication year - 2021
Publication title -
journal of pharmaceutical research international
Language(s) - English
Resource type - Journals
ISSN - 2456-9119
DOI - 10.9734/jpri/2021/v33i43a32460
Subject(s) - face (sociological concept) , computer science , chin , artificial intelligence , computer vision , domain (mathematical analysis) , facial recognition system , pattern recognition (psychology) , medicine , mathematics , anatomy , mathematical analysis , social science , sociology
In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. In the recent scenario, the entire globe is facing enormous health risks occurred due to Covid-19. To fight against this deadly disease, consumption of drugs is essential. Consumption of drugs may provide some abnormalities to human face. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. To assess these human face abnormalities, the application of computer vision is favoured in this study. This work analyses an input image of human’s frontal face and performs a segregation method to separate the abnormal faces. In this research work, a method has been proposed that can detect normal or abnormal faces from a frontal input image due to COVID-19. This method has used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.