Modern deep CNN face matchers are trained on datasets containing "color" images. We show that such matchers achieve essentially the same accuracy on color images when trained using only grayscale images. We then consider possible causes for deep CNN face matchers "not using color". Popular web-scraped face datasets actually have 30 to 60% of their identities with one or more grayscale images. We analyze whether this grayscale element in the training set impacts the accuracy achieved, and conclude that it does not. Comparable accuracy for color test images using only grayscale images implies that the inclusion of "color" may not necessarily add any significant information to the recognition of individuals. This also implies the use of computing resources can be optimized to make the training process more efficient using only grayscale images. Utilizing grayscale images for training reduces the memory footprint of the training data, thereby decreasing system processing time during training. Additionally, our findings emphasize that the adoption of grayscale images not only makes face recognition training more efficient but also offers the opportunity to include more training data, which could result in more accurate face recognition models.