Automatic face recognition (FR) problem has been extensively studied and applied to domains including biometrics, security, authentication, surveillance, and identification. Face recognition algorithms commonlly use high dimensional information and are therefore computationally expensive. The use of wrongly detected features can confuse the recognition process and make it even slower. This paper presents a set of heuristic lightweight features to describe eye regions. These features are used to further classify detected eye regions into false positive and positive ones. Note that the detected eye regions are all very similar, making it hard to find a good set of features to separate them. The classification is done by simply applying a threshold proportional to the variance of the data. The method was able to correctly classify 49.8% of the false positive samples, which if applied to Viola and Jones' best result could potentially turn its 93.7% performance into 96.8%. If the classification considers the pairs heuristics, the performance goes up to 83.3% at the expense of wrongly classifying positive samples. It would make the 93.7% become 98.9%.