Facial expressions recognition using discrete Hopfield neural networks

Two kinds of discrete type Hopfield neural networks are used to recognize the facial expressions. It is expected that the associative memory effect of the Hopfield neural networks is capable of classifying the preliminarily defined specific expressions among the variety of facial expressions. Four kinds of facial expressions such as “surprise”, “anger”, “happiness” and “sadness” expressed by 10 persons are used as the input data to be recognized. The each image of the faces under test are divided into 8×10 regions as the feature area. Accordingly, 8×10 ternary values [+1 (moving upward), 0 (no movement), -1 (moving downward)] computed from the average value of the optical flows in each region are used as the feature parameters. Two kinds of neural networks trained by different learning data are cascade-connected to compensate each other. The experimental results showed that the averaged recognition rate for those 4 expressions was 92.2%