Machine Learning-based Decision Support System for Vehicle and Human Gender Classification

Nowadays, various electronic devices such as digital cameras, smart phones, or even handheld gaming consoles help in obtaining digital photos or shoot short videos. Moreover, images and videos are becoming a part of our daily routine as they remain as a source of occasion and memory of important occurrences in life. For the Human Visual System, the perception of visual feature (i.e., images or videos) can be understood with ease and will throw people back to the happiest moments of life and make them remember someone important said by many psychologists.

Usually, roads are systematic in traffic management, yet there is a difficulty in solving problems concerning visually impaired persons. Even normal people get stuck in the traffic and meet with accidents. When it comes to blind people, the issue is even more serious and everyone abuses them as if they are responsible for the accident. It is a common occurrence found on regular roads that rash drivers, despite their own mistakes, always blame others for their wrongdoing.
In addition, there is an issue of hiring people with poor eyesight as vehicle drivers, which causes 81% of the accidents on roads. Although this has been happening, no tests are being conducted for drivers regularly in private travel companies. The government officials must ensure eye testing for the drivers regularly. On the other hand, around 26% of accidents involving these drivers may be due to the carelessness of the opposing parties or the vehicle drivers.

Nevertheless, when it comes to pedestrians, blind people are often helped by someone to cross the roads but sometimes the blind try it on their own. There is a possibility of an accident occurring in the latter case. This problem has motivated many to think of developing any useful AI interface unit.

Face-based gender Recognition (FR) is probably the most dynamic application area that is imaginative and realistic and it has several guidelines to follow at various stages in the process of achieving accuracy.

Software-driven automatic Face-based gender Recognition (FR) is probably the most dynamic field in Machine Learning research, attracting numerous proposals in recent years. On a human face, looks can be seen from facial muscle groups and these are sometimes not impacted through inward feeling states.