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Hello, I am testing the LBPH face recognition method varying its parameters, but I don't quite understand the "effect" of each parameter.

The documentation says:

• radius – The radius used for building the Circular Local Binary Pattern. The greater the radius, the

• neighbors – The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.

• grid_x – The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• grid_y – The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

1. The documentation says: "The greater the radius, the". What happens when I use a greater radius?

2. As the documentation says in the neighbor's parameter: "the more sample points you include, the higher the computational cost", but what is the benefit of a greater "sample points"?

3. What is the benefit of using higher grid_x and grid_y? It will increase the computational cost, right? The documentation says: "The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.". It means that the vector will be more accurate to represent that face?