In this 2-hour long project-based course, you will build and train a convolutional neural network CNN in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. This course runs on Coursera's hands-on project platform called Rhyme.
Facial Expression Recognition
Facial Expression Recognition | Papers With Code
Emotion plays an important role in communication. For human—computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks DNNs are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes.
Facial Expression Recognition with LBP and ORB Features
To gain accurate and reliable data about facial expressions, FaceReader is the most robust automated system that will help you out. Many researchers have turned towards using automated facial expression analysis software to better provide an objective assessment of emotions. FaceReader software is fast, flexible, objective, accurate, and easy to use.
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