  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.
