Facial expression detection using Artificial neural network

Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Human Sate Recognition as a facial expression recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. In this paper all five universally recognized basic emotions namely angry, disgust, happy, sad and neutral. This paper presents a new technique for facial expression recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image compression using the two-dimensional discrete cosine transform (2D-DCT). The DCT extracts features from face images based on skin color. Feature vectors are constructed by computing DCT coefficients. A self-organizing map (SOM) using an unsupervised learning technique is used to classify DCT-based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. Facial expression recognition with SOM is carried out by classifying intensity values of grayscale pixels into different groups. Evaluation was performed in MATLAB using an image database of 25 face images, containing five subjects and each subject having 5 images with different basic facial expressions