Face Emotion Recognition - DeepCNN Python

Emotion Recognition using Tensorflow, simple and easily understandable code.

Introduction

The most common application of CNN in Computer Vision technology is Image processing. Given the images as input, RGB or BW, we use underlying the pixel data to extract specific information, based on the given label. We then train a CNN network.

If you are just getting started into Computer Vision, the simplest problem to work on would be, Cat and Dog classifier, which you study here.

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Problem statement

The task at hand is to build a Face emotion recognition model, in simple terms an Emotion Classifier, from a given Image input. You can find more details of the Problem and the dataset here.

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Data Overview

The given data consists of, (35887, 3) datapoints rows and 3 column features.

1. emotion - numerical value to indicate the type of emotion, 0–2 being negative, 3–5 being positive and 6 indicates neutral emotion.

2. Pixels - represents the pixel coordinate point of the image.

3. usage - data split

emotion label - 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'surprise', and 'neutral'.