Use Interactive Videos for the Best Marketing

With significant growth in digital marketing and that too enormously on video marketing, now the marketers have realized high-quality interactive video production to capture audience attention. The content needs to be creative as well as being brilliantly engaging it should be an interactive video platform. Consider today's reality where there are thousands of videos uploaded every minute on YouTube alone- this is the kind of competition we need to consider. So we need to be our best to be relevant and effective in the eyes of customers.


Most of the companies are now indulging in video; world internet traffic will be mostly in videos in the coming days. With such significant growth in digital marketing, enormously around, the video content marketers realized that only high-quality video production does not serve the purpose anymore to capture their audience's attention. The content has to be creative with engaging ideas. Rather it should be more interactive video platform for the customer so they are engaged with it.

Facial expression recognition (FER) has emerged as an important research area over the last two decades. Facial expression is one of the immediate, natural, and powerful means for humans to communicate their intentions and emotions. The FER system can be used in many important applications such as driver safety, health care, video conferencing, virtual reality, and cognitive science, etc.


Generally, facial expression can be classified into neutral, anger, disgust, fear, surprise, sadness, and happiness. Recent research shows that the ability of young people to read the feeling and emotion of other people is getting reduced due to the extensive use of digital devices. Therefore, it is important to develop a FER, Facial expression recognition system which accurately recognizes facial expression in real-time. An automatic FER system commonly consists of four steps: Preprocessing, feature extraction, feature selection, and classification of facial expressions. In the preprocessing step, the face region is first detected and then extracted from the input image because it is the area that contains expression-related information.

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