SVM point-based real-time emotion detection

Facial Emotion Recognition Using Machine Learning

“…This method uses cascaded regression trees and finds the important positions on the face using images. Pixel intensities are used to distinguish between different parts of the face, identifying 68 facial landmarks [1]. Based on a current estimate of shape, parameter estimation is done by transforming the image in the normal co-ordinate system instead of global.…”

“…Extracted features are used to re-estimate the shape parameter vectors and are recalculated until convergence [5]. The author [1] uses only 19 features as shown in Figure 3 from the 68 extracted features, focusing only around eyes, mouth and eyebrows.…”

“…It can be seen as a second step to face detection where we may be required to set up a second layer of security, where along with the face, the emotion is also detected. This can be useful to verify that the person standing in front of the camera is not just a 2-dimensional representation [1].…”

“…The most important aspects for any machine learning algorithm are the features. In this paper we will see how the features are extracted and modified for algorithms like Support Vector Machines [1]. We will compare algorithms and the feature extraction techniques from different papers.…”

“…Ensemble of regression trees was very fast and robust giving 68 features in around 3 milliseconds. The parameters tuned for the algorithm are shown in Table 1 [1]:…”