Face Shape Classification based on Deep Face

Industry 4.0 combines virtual and physical worlds, enabling cyber-physical systems (CPS) through computer vision. The ability of computers to understand and interpret visual data opens up numerous possibilities for innovation and efficiency in various domains. Face shape classification is crucial for selecting eyelashes, hairstyles, makeup, and glasses frames based on guidelines and personal preferences. Automated face shape identification systems can alleviate this time and effort. Existing methods face challenges due to face geometry and variations. Computer vision has been affected by the appearance of profound learning due to the need to characterize or translate the substance of regular pictures, the availability of large datasets, and the creation of convolution layers that could be run quickly on GPUs with remarkable accuracy. This work presents an approach for classifying face shapes into five types: oval, heart, oblong, square, and round.