Facial Expression RecognitionUsing Artificial Neural Networks

Abstract

• Use of the two-dimensional (2-D) discrete cosine transform(DCT) over the entire face image as a feature detector and a rbfn one-hidden-layer neural network as a facial expression classifier.

• RBFN is well suited to handle static images, since the receptive field centers capture prototypical images from the application, which in turn directly capture two dimensional spatial information. The RBFN architecture, however, is not Well suited to handle time-varying images

RBFN

After computation of the reduced vectors of dataset, radial basis function neural network is used to train our neural network. The process learning is as follow:

• Read data and specify the desired output for each vector.

• Randomly initialize weights and bias.

• Then gradually adjust the weights of the network by performing the following procedure for all vectors.

• Compute the hidden layer and output layer neuron using radial function.

• - Calculate errors of the output and hidden layers.

Adjust weights:

With PFA

• we encountered problems in these experiments due to subject rigid motions and subject orientation.

• The neural network learns to correlate motions in certain parts of the image retina with specific expression. When these motion patterns are offset by rigid motion, they do not register properly with the neural network.

Thank You

Made by -Mitul Rawat-Naveen-Shubham Gupta

IIT ROORKEE