I've created the the framework of the system, which takes a picture, converts it to an LBPH image, and then gets the histograms from each tile of the grid(8x8). I'm following this paper on it, but am confused what to do next to identify features after step 4. Do I just compare each square of the grid with a set of known feature squares and find the closest match? This is my first facial detection program so I'm very new to it.
So basically image processing works like this. Pixel intensity values are way too variant and uninformative by themselves to be useful for algorithms to make sense of an image. Much more useful is the local relationships between pixel intensity values So image processing for recognition, detection is basically a 2-step process.
Feature Extraction - Transform the low-level, high variance, uninformative features such as pixel intensities into a high-level, lower variance, more informative feature set (e.g. edges, visual patterns, etc.) this is referred to as feature extraction. Over the years, there have been a number of feature extraction mechanisms suggested such as edge detection with Sobel filters, histogram of oriented gradients (HOG), Haar-like features, Scale invariant features (SIFTS) and LBPH as you are trying to use. (Note that in most modern applications that are not computationally limited, convolutional neural networks (CNNs) are used for the feature extraction step because they empirically work much much better.
Use Transformed Features - once more useful information (a more informative set of features) has been extracted, you need to use these features to perform the reasoning operation you're hoping to accomplish. In this step, you fit a model (function approximator) such that given your high-level features as an input, the model outputs the information you want (in this case a classification on whether an image contains a face I think). Thus, you need to select and fit a model that can make use of the high-level features for classification. Some classic approaches to this include decision trees, support vector machines, and neural networks. Essentially, model fitting is a standard machine learning problem, and will require using a labelled set of training data to "teach" the model what the high-level feature set will look like for an image that contains a face, versus an image that does not.
It sounds like your code in its current state is missing the second piece. As a good starting place, look into using sci-kit learn's decision tree package.