COMPUTER VISION:Ĭomputer vision provides the ability for the computer to see as humans see. For more information on Deep Learning, you can visit this link. It is self-capable of learning from the extraordinary amount of data provided to it. Deep Learning models don’t require a human programmer to intervene and tell what to do with the data. In Deep Learning, the model learns through an artificial neural network that is very much similar to a human brain and this allows the model to analyze data in a structure much similar to humans do. Deep Learning represents the next evolution in Machine Learning. Machine Learning (ML) and Deep Learning are subsets of Artificial Intelligence. In this blog, we will be using Deep Learning and Computer Vision. The faces have been automatically registered such that the face is more or less centered and occupies about the same amount of space in each image. Each image in this dataset is labeled as one of seven emotions: happy, sad, angry, afraid, surprise, disgust, and neutral. The FER-2013 dataset consists of 28,709 labeled images in the training set and 7,178 labeled images in the test set.
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This model is capable of recognizing seven basic emotions as following:
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In this blog, we will build a Convolution Neural Network (CNN) architecture and train the model on FER2013 dataset for Emotion recognition from images. Borrowing from our vast experience working on Deep Learning and Machine Learning problems, we have created a Deep Learning Model which is capable of accurately recognizing human emotions through rigorous training using superior quality datasets. Introduction to Convolution Neural Network (CNN) and OpenCV in Emotion RecognitionĬlairvoyant is at the forefront of harnessing the power of data to reap actionable insights for our clients through AI-, ML-, and related data solutions.