Now that we have seen how to implement a generic SVM classifier using OpenCV/C++, in this section, we outline the steps to use SVM for the gender classification project that we have been working on.
If you noticed in the example that we discussed in the last section, the training data that we loaded was 2-dimensional and had 10 data points. In the previous chapter, we discussed the fact that we are going to represent our faces using the 531-dimensional uniform pattern LBP histogram descriptor. This means that each data point (face) will be represented using 531-dimensions. These values (the feature vector corresponding to the representation of a face) are usually read into the source code through text files. This means that we design our program to accept two files as input, one holding the feature vectors of the faces in the training data set and the other for the test data.
So essentially, this means that we want the feature descriptors of all our face...
