Abstract
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.
Highlights
A face is a key feature to identify a human
The images are taken from the dataset: https://www.kaggle.com/ciplab/real-and-fakeface-detection
In addition in this experiment, the World Wide Web (WWW) repository is used for requires images to create the primary dataset
Summary
A face is a key feature to identify a human. During the early stage of embryogenesis development genetic factors often play a key role to develop a face. It was found the changes due to the effect of drugs to be given in hospitals as well as camp for vaccination of COVID-19. In this modern era with advanced research technologies, it is possible to identify this facial disorder with help of various methods. The primary objective of this study is to detect whether a human face is normal or abnormal. To implement this process in this research first face and facial detection system are developed. The findings of this research suggested that this proposed methodology is useful in this research area and can lead more objective research in the future
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