People rapidly form impressions from facial appearance, and these impressions affect social decisions. Data-driven, computational models are the best available tools for identifying the source of such impressions. However, the computational models cannot be accepted unless they have passed the tests of validation to ascertain their credibility.

In this paper, the condition of the eyes of the person is used to validate the fuzzy rules extracted from the computational models. A simple and effective classifier is proposed to evaluate the closeness of the eyes during the evaluation of a small database of portraits. The experimental results show that closed-eyes can be detected only after the proposed shift of the normalized histogram is applied. Although it is very simple, the proposed classifier can achieve better accuracy than other state of the art classifiers. The relationship between the closeness of the eyes and the evaluation of the subjects is also analyzed.

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