Facial-Expression-Recognition using Tensorflow

Facial Emotion recognition is very easy task for human, as we have a very complex and sophisticated biological neural network in our brain which has been trained since we born. But it is very difficult task for computer machines. Here I provide a neural network implementation to perform facial expression recognition. It implements a simple but efficient convolution neural network using most popular library tensorflow.

Prerequisites

• Tensorflow version latest by 1.1, see how to install
• Csv lib
• Knowledge of deep learning concepts, if you don't feel comfortable working with cnn then you can use online book by Michael Nielsen.
• Facial expression data set must be available on your system, download here

Data-sets

The available data sets contains 7 basic emotions: happy, sad, disgust, surprise, fear, anger and neutral. It comprises a total of 35887 pre-cropped, 48-by-48-pixel grayscale images of faces each labeled with one of the 7 emotion classes. This tells that our cnn model outputs either probabilities or class score into 7 classes. I used 28672 number of images for training our neural network model and 7168 number of images for testing purpose.

The Model

It uses csv python module to open given csv file into appropriate csv module. Here we use 5 layers.
	1. Convolutional layer 
		Input  : 4d tensor, dim:[N, w, h, Number of input channel = 1], where N is batch size.
		Output : 4d tensor, dim:[N, w/2, h/2, Number of filters at cnn layer-1]

	2. Convolutional layer 
		Input  : 4d tensor, dim:[N, w/2, h/2, Number of filters at cnn layer-1]
		Output : 4d tensor, dim:[N, w/4, h/4, Number of filters at cnn layer-2]

		Now this output 4d tensor is flattened inorder to provide input to fully connected layer-1.

	3. Fully connected layer
		Input  : 2d tensor, dim:[N, Flattened size]
		Output : 2d tenser, dim:[N, Number of neurons at fully connected layer-1]

	4. Fully connected layer
		Input  : 2d tensor, dim:[N, Number of neurons at fully connected layer-1]
		Output : 2d tenser, dim:[N, Number of neurons at fully connected layer-2]

	5. Output layer.
		Input  : 2d tensor, dim:[N, number of neurons at fully connected layer-2]
		Output : 2d tenser, dim:[N, Number of classes]

How to run

Simply run python file.

Model graph

Plot between Cost and Epochs

Plot between Training Accuracy and Epochs

Plot between Testing Accuracy and Epochs

About me

I am a computer programmer who loves to solve programming problems and exploring the exciting possibilities using deep learning. I am interested in solving real life problems using efficient algorithms and computer vision that creates innovative solutions to real-world problems. I hold a B.Tech degree in computer Engineering From Nit kurukshetra. You can reach me on LinkedIn.