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Mood detection of psychological and mentally disturbed patients using Machine Learning techniques


Muhammad Gulraj, Nasir Ahmad


Vol. 16  No. 8  pp. 63-67


This paper describes a simple and novel approach for detection of mood of a psychological patient using face images. We developed a system using different machine learning techniques to automatically classify and detect the mood of the psychological or mentally disturbed patient under observation. We can divide the main problem into three sub parts. In the first step we divided the mood of a person into five distinct classes i.e. Sad, Angry, Happy, Normal and Surprised. For every class we use a set of input images to train the K nearest neighbor (KNN) classifier. In our system we used Speeded-up robust features (SURF) for detection of local features and descriptors from the input sets of images. These features are used for training KNN classifier. In the second step we extract SURF features from test image, i.e. when a test image is passed to the system for classification, the system uses the SURF technique for extraction of features and descriptors from the test image. In third step, the descriptors and features extracted from test image are passed to the trained KNN classifier, which classify the image into one of the classes. After performing 250 experiments for each class, we got an over all accuracy of 77.4% for the classifier. This is a novel approach to detect the mood of a person using face images.


Mood, SURF, Machine Learning, Psychological patients