What is the most Accurate way of Determining an Object's Color? I have written a computer program that can detect coins in a static image (.jpeg, .png, etc.) using some standard techniques for computer vision (Gaussian Blur, thresholding, Hough-Transform etc.). Using the ratios of the coins picked up from a given image, I can establish with good certainty which coins are which. However, I wish to add to my confidence levels and also determine if a coin that I deduce to be of type-A (from radius ratios) is also of the correct colo[u]r. The problem is that for British coins et al. (copper, silver, gold), the respective colors (esp. of copper to gold) are very similar.

I have a routine that extracts the mean color of a given coin in terms of the RedGreenBlue (RGB) 'color-space' and routines to convert this color into HueSaturationBrightness (HSB or HSV) 'color-space'. 

RGB is not very nice to work with in attempting to differentiating between the three coin colors (see attached [basic] image for an example). I have the following ranges and typical values for the colours of the different coin types:

Note: the typical value here is one selected using a 'pixel-wise' mean of a real image.

Copper RGB/HSB: typicalRGB = (153, 117, 89)/(26, 0.42, 0.60).

Silver RGB: typicalRGB = (174, 176, 180)/(220, 0.03, 0.71).

Gold RGB: typicalRGB = (220, 205, 160)/(45, 0.27, 0.86) 

I first tried to use the 'Euclidian distance' between a given mean coin color (using RGB) and the typical values for each coin type given above treating the RGB values as a vector; for copper we would have: 

$$D_{copper} = \sqrt((R_{type} - R_{copper})^{2} + (G_{type} - G_{copper})^{2} + (B_{type} - B_{copper})^{2})$$

where the smallest value of the difference ($D$) would tell us which type the given coin is most likely to be. This method has shown itself to be very inaccurate.

I have also tried just comparing the hue of the coins with the typical values of the types provided above. Although theoretically this provides a much better 'color-space' to deal with varying brightness and saturation levels of the images, it too was not accurate enough.

Question: What is the best method to determine a coins type based on color (from a static image)?

Thanks very much for your time.
You can fit a model to estimate a posterior probability of a color space point to belong to a certain coin type. Take a look at Gaussian mixture model. Another (somewhat arbitrary, but simpler) approach is to use nearest-neighbour interpolation.