Abstract This application is based on computer vision it classify the human facial expression using deep neural network . There are certain advance algorithms which makes possible to detect emotions from images and real time videos . Here in this project we have our generated model using haar cascade classifier and convolutional neural network to classify the emotions as “Angry , Happy,Neutral,Sad,Surprise ” . Haar Cascade classifier is from the open source computer vision it detects the faces from a given image or frames . After getting the output from the classifier then using the neural network model to analyse and detect what kind of emotions is having in the given input . For neural network model we have trained the datasets using convolutional neural network as it takes image directly as input ,gives high degree of accuracy when use in several layers .It has important properties like weight sharing , sparse vector, feature selection etc. Vision Facial Expression are complex and it is necessary to understand the person intentions , emotions . As this gestures are extremely important to have particular response. The application aims to improve the quality of human –machine interaction , to make system as more intelligent that impact human lives to be in more positive way. This application can also helps in several sector like in health care , gaming , finance industry. As the communication is non-verbal it quiet make s difficult for the machine to classify .But due to advancement in deep neural network some how we have reached certain accuracy that helps in identifying the human face expressions . Introduction General Workflow • This recognition system designed to determine the emotional state of a person’s facial expressions . • Initially, the system analyzes the image of the face, locates and measures the characteristic . • Then each part of the face is analyzed deeper and its features are extracted. Features are presented as information vectors. • The classification of vector features according to the corresponding expression emotions is carried out by a multilayer neural network, which is trained and used to classify facial expressions according to the corresponding emotional category. Scope of the project in the industrial usage : Emotion recognition is already widely used by different companies and can be used in society for a variety of reasons . Several businesses can process images, and videos in real-time for monitoring video feeds or automating video analytics, thus saving costs and making life better for their users. In certain domains like Healthcare Domain : Emotion recognition can be helpful for people who are visual impaired , so they can have effectiveness in their communication by detecting facial expressions . There is an application ready already from Affectiva which is an emotion measurement technology from MIT’S media lab.They have developed software to recognize human emotions on facials cues or physiological responses . This technology enables software applications to use a webcam to track a user’s smirks,smiles,frowns and furrows , which measures the users level of surprise , amusement or confusion . The technology also allows a person's heart rate to be measured from a webcam without the person wearing a sensor. This is accomplished by tracking color changes in the person's face, which pulses each time the heart Emotion Recognition in Automotive Industry The Automative industry also enhancing their product with emotion recognition technology . As car manufactures they emphasize more on making cars more personal and safe for people to drive . Using AI to help them understand the human emotions. Facial emotion detection smart cars can alert the driver when he is feeling drowsy. In gaming industry Some video games create survey kind of while in testing phase of their product .During this phase user asked to play game for certain amount of time . Using facial recognition can be help to understand the which kind of emotions an user is experiencing in real time . Tracking emotion is can help the developers to make their game less violent with more user friendly . Thus it really have the industrial impact ,but can it be trusted It does evaluate decisions at our facial expression .It automate certain communication and understandings but question arises that really we want the outcomes of our results being determined on this basis. https://www.theverge.com/2019/7/25/8929793/emotion-recognition-analysis-ai-m achine-learning-facial-expression-review https://en.wikipedia.org/wiki/Emotion_recognition#Applications https://sightcorp.com/knowledge-base/emotion-recognition/