Automatic human facial expression recognition using Hidden Markov Model

Facial Recognition is a type of biometric software application that can identify a specific individual in a digital image by analyzing and comparing patterns. These systems are commonly used for the security purposes but are increasingly being used in a variety of other applications such as residential security, voter verification, banking using ATM. Changes in facial expression become a difficult task in recognizing faces. In this paper continuous naturalistic affective expressions will be recognized using Hidden Markov Model (HMM) framework. Active Appearance Model (AAM) landmarks are considered for each frame of the videos. The AAMs were used to track the face and extract its visual features. There are six different facial expressions considered over here: Happy, Sadness, Anger, Fear, Surprise, Disgust, Fear and Sad. Different Expression recognition problem is solved through a multistage automatic pattern recognition system where the temporal relationships are modeled through the HMM framework. Dimension levels (i.e., labels) can be defined as the hidden states sequences in the HMM framework. Then the probabilities of these hidden states and their state transitions can be accurately computed from the labels of the training set. Through a three stage classification approach, the output of a first-stage classification is used as observation sequences for a second stage classification, modeled as a HMM-based framework. The k-NN will be used for the first stage classification. A third classification stage, a decision fusion tool, is then used to boost overall performance.