TY  - BOOK
AU  - Rajalekshmi M. G. (93517010)
TI  - Facial expression recognition system with five layer convolution neural network
KW  - FACIAL EXPRESSION RECOGNITION
KW  - CNN
KW  - MACHINE LEARNING
N1  - Abstract 

Now a days facial expression recognition field is growing and playing important 
role in communication. Facial expression recognition has been an active research area in 
the past ten years, with growing application areas including avatar animation, neuro 
marketing and sociable robots. The recognition of facial expressions is not an easy 
problem for machine learning methods, since people can vary significantly in the way 
they show their expressions. However, facial expressions change so subtly that 
recognition accuracy of most largely depend on feature extraction. In this work, we 
propose two independent methods for this very task. The first method uses facial 
expression recognition system using with five layer convolutional neural network, while 
the second method is an 8-layer convolutional neural network (CNN) or Alex net.In the 
first method to achieve facial expression recognition based on a deep CNN. Firstly we 
implement face detection by using Haar-like features and histogram equalization. Then we 
construct a five-layer CNN architecture, including two convolutional layers and two 
subsampling layers (C-S-C-S). Finally, a Softmax classifier is used for multi-
classification.Second method,created an 8-layer CNN with five convolutional layers, and 
three fully connected layers. This module, to reduce redundancy of same features learned, 
considers mutual information between filters of the same layer, and processes the best set 
of features for the next layer. 

ER  -