Real-time Emotion and Gender Classification using Ensemble CNN
This work addresses the problem of automated emotion and gender recognition for applications in human-machine interfaces, surveillance, and customer feedback, but it is incremental as it builds on existing CNN methods with ensemble techniques.
The paper tackled real-time emotion and gender classification from facial images using an Ensemble CNN, achieving 68% accuracy for emotion classification on the FER-2013 dataset and 95% for gender classification on the IMDB dataset, with a processing time of less than 0.5 seconds for real-time inputs.
Analysing expressions on the person's face plays a very vital role in identifying emotions and behavior of a person. Recognizing these expressions automatically results in a crucial component of natural human-machine interfaces. Therefore research in this field has a wide range of applications in bio-metric authentication, surveillance systems , emotion to emoticons in various social media platforms. Another application includes conducting customer satisfaction surveys. As we know that the large corporations made huge investments to get feedback and do surveys but fail to get equitable responses. Emotion & Gender recognition through facial gestures is a technology that aims to improve product and services performance by monitoring customer behavior to specific products or service staff by their evaluation. In the past few years there have been a wide variety of advances performed in terms of feature extraction mechanisms , detection of face and also expression classification techniques. This paper is the implementation of an Ensemble CNN for building a real-time system that can detect emotion and gender of the person. The experimental results shows accuracy of 68% for Emotion classification into 7 classes (angry, fear , sad , happy , surprise , neutral , disgust) on FER-2013 dataset and 95% for Gender classification (Male or Female) on IMDB dataset. Our work can predict emotion and gender on single face images as well as multiple face images. Also when input is given through webcam our complete pipeline of this real-time system can take less than 0.5 seconds to generate results.