Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

Face Emotion Detection

Humans have always had the innate ability to recognize and distinguish between faces. Now computers are able to do the same. This opens up tons of applications. Face detection and Recognition can be used to improve access and security like the latest Apple Iphone does (see gif below), allow payments to be processed without physical cards — iphone does this too!, enable criminal identification and allow personalized healthcare and other services. Face detection and recognition is a heavily researched topic and there are tons of resources online. We have tried multiple open source projects to find the ones that are simplest to implement while being accurate.

Humans are used to taking in non verbal cues from facial emotions. Now computers are also getting better to reading emotions. So how do we detect emotions in an image? We have used an open source data set — Face Emotion Recognition (FER) from Kaggle and built a CNN to detect emotions. The emotions can be classified into 7 classes — happy, sad, fear, disgust, angry, neutral and surprise.

Model — We built a 6 layered Convolutional Neural Network (CNN) in Keras and use image augmentations to improve model performance. We tried many different models and have open sourced our best implementation at this link.

You can load the pretrained model and run it on an image using only 2 lines of code below:

model = load_model("./emotion_detector_models/model.hdf5")
predicted_class = np.argmax(model.predict(face_image)