Color is inaccessible for individuals who are blind or visually impaired, as it is a purely visual feature. Given that many everyday tasks rely on color including coordinating clothing, social interactions, etc., the inaccessibility of color has an adverse effect on daily life. We propose an interactive, wearable assistive device that can recognize and convey colors of scenes or objects. As computer vision is challenging in real world environments due to, e.g., illumination or pose changes, computer vision algorithms can be augmented with sub-systems that can provide information on working environments of a recognition algorithm, and how it affects the recognition accuracy. In this paper, we introduce a framework that incorporates such measures, herein called confidence measures, in wearable assistive devices. By communicating to the user a quantitative measure that signifies the difference between optimal working conditions and the real environment working conditions, we can convey the reliability of system-made decisions, which enables the user to take action to improve confidence. Given that color recognition is challenging in real world settings, our system is built within our proposed framework for confidence measures. Finally, we present user recognition accuracies, both with and without confidence measures.