Face Detection
The primary objective is to be able to detect faces and also analysis the facial feature and expression so as to identify attributes such as gender, smile, age of people and also whether the person is wearing glasses.
Approach OpenCV library is used to detect and segment faces from video images:
1.Using a cascade of boosted classifiers working with haar-like features.
2.Training classifiers by a database of face and non-face images.
3.Input images are scanned at different scales to find regions that are likely to contain faces.
Gender | Glasses | Smile Detection
We use the SVM classifier method: data points are dealt with as a p-dimensional vector. We use the library LibSVM.
stages of the image processing:
1.Segment out face rectangle
2.Scale to 24*24 grayscale image
3.Equalize histogram to increase contrast
4.Scale the intensity to [-1, 1]
5.Form a 576 (24x24) dimensional vector
6.Train the gender model with LIBSVM.