Humans are used to taking in non verbal cues and signals from facial emotions. As the human- machine interactions grow, computers are also getting better at reading emotions. With the use of deep learning, emotions can be detected based on the learned classes. Emotions and sentiments that can be detected include but not limited to: Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neural expression.
One of the most common computer vision applications is facial recognition. A face recognition system compares a human face from a digital picture or video frame to a database of faces using computer vision technologies. Deep learning algorithms can recognize human faces in real time by analyzing video feeds from digital security cameras or webcams.
The ability to predict gender from facial images is used in many industries, including surveillance, biometrics and marketing. Yet, predicting gender from images is a challenging problem, the use of deep learning has made gender predection possible and more accurate.
Aknowledging, understanding and respecting different generations is essential for efficient and profitable teams, departments, and businesses. There are five different generations in the workplace: Traditionalists, Baby Boomers, Generation X, Generation Y/Millennials, and Generation Z. The use of deep learning in determining what generation you and your coworkers belong in, can help resolve several intergenerational conflicts.
Over the past few years, security concerns have increased. The use of deep learning can secure, and control access to schools, airports, and businesses. It ensures that people are safe and protected at any time. Authorities and businesses could react more efficiently when alerts are triggered.
With the growing development of deep learning models, it is possible to predict age from pictures. It can help marketing campaigns to be more targeting, prevent children from accessing inapropriate content and boost security levels.
Preventing dicriminatory, offensive and biased results without any context can prove to be challenging. The use of deep learning in multicultural appearance detection (Middle Eastern, Latino_Hispanic, East Asian, Black, Southeast Asian and Indian, and White) can help raise awarness on social and cultural problems.