There is a long history of relating to the recognition of facial expressions of emotion that can be traced back to Darwin in the late 1800’s. Darwin considered that facial expressions of emotion was an innate, adaptive, and physiological response which could provide evidence of an individual’s internal mental state. There were various early ways of measuring internal mental states that included attempts at accuracy in the measurement of the facial muscle movement

1. Facial Expression Recognition
Using Artificial Neural Networks

2. 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

4. 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.

5. • - Calculate errors of the output and hidden layers.
Adjust weights:
With PFA

6. • 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.

8. Thank You
Made by
-Mitul Rawat
-Naveen
-Shubham Gupta
IIT ROORKEE