Automatic Facial Expression Analysis and Recognition using Zone Based Active and Salient Facial Patches

Abstract

Recognition of facial expression has many useful applications that have drawn researcher’s interest over the past decade. Extraction of features is a major step in the analysis of expression which leads to fast and accurate recognition of expression. Recognition of facial expressions is not an easy issue for methods of machine learning, as different people can vary in the way they show their expressions and for one expression the image of the same person can differ for brightness, background and position. Recognition of facial expression is therefore still a challenging computer vision problem. In this thesis work, we aim to design a robust technique of automatic facial expression analysis and recognition using zone based active and salient patches of the human face by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. Facial expression recognition (FER) can also be considered as a special case of a pattern recognition problem and many techniques are available. In the designing of an FER system, we divided the system into 4 modules, i.e. preprocessing, active and salient patch extraction and classification. Voila Jones algorithm is used for face detection and after that features are extracted from the facial patches. The active facial patches are located on the facial regions that during different expressions undergo a major change. The active patches are located after detection of facial landmarks and hybrid features are determined from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost. Zoning is applied and got remarkable results. The dimensionality of the function is reduced by using linear discriminant analysis, which is further defined using the support vector machine (SVM). On the basis of classification expression is recognized. We evaluated our algorithm on Extended Cohn-Kanade (CK+) dataset.