FACE EXPRESSION DETECTION USING OPENCV
Keywords:Haar cascade, Fisherface, OpenCV, Numpy, Adaboost.
Computer vision is a field which teaches computers to gather information they see. It is a way computers learn from their environment and meet our demands. In the past few years face recognition has become popular and is being extensively used in various computer vision fields. This has motivated researches and neuroscientists to dig more on expression detection since it has many applications inautomatic access control system. In this work, we detect user’s emotions using his facial expressions. The expressions are predicted from the pre-existing image available in the memory and also real time. In order to build a face expression detection system, we scan the training dataset and compare it with the test data which helps the classifier to predict the expression. This implementation is done using Haar-cascade algorithm in order to detect the face. Due to its efficiency, Haar-like rectangle features play an important role in face detection. Also, an algorithm known as fisher face is used to detect the expression from the given dataset loaded by the user. These algorithms are provided by OpenCV which is an open source computer vision library with various kinds of algorithms built in. It is applicable in various computer vision projects. This work has been implemented using Python Idle 2.7, Open Source Computer Vision Library (OpenCV) and NumPy.