Is conversion to gray scale a necessary step in Im

Question

I would like to know if converting an image to gray scale is necessary step for all image pre processing techniques. I am using a neural network for face recognition. Is it really necessary for converting it into a gray scale or can I give color images also as input to neural networks?

Answer 1

No, it is not required, it simplifies things, so it is an often practice to do so, but in general you could work directly on the color image, in any representation (RGB, CMYK) by simply using more dimensions (or more complex similarity/distance measure/kernel).

Answer 2

Converting to gray scale is not necessary for image processing, but is usually done for a few reasons:

1. Simplicity - Many image processing operations work on a plane of image data (e.g., a single color channel) at a time. So if you have an RGBA image you might need to apply the operation on each of the four image planes and then combine the results. Gray scale images only contain one image plane (containing the gray scale intensity values).
2. Data reduction - Suppose you have a RGBA image (red-green-blue-alpha). If you converted this image to gray scale you would only need to process 1/4 of the data compared to the color image. For many image processing applications, especially video processing (e.g., real-time object tracking), this data reduction allows the algorithm to run in a reasonable amount of time.

However, it's important to understand that while there are many advantages of converting to gray scale, it is not always desirable. When you convert to gray scale you not only reduce the quantity of image data, but you also lose information (e.g., color information). For many image processing applications color is very important, and converting to gray scale can worsen results.

To summarize: If converting to gray scale still yields reasonable results for whatever application you're working on, it is probably desirable, especially due to the likely reduction in processing time. However it comes at the cost of throwing away data (color data) that may be very helpful or required for many image processing applications.