Five types of discriminatory features are derived using a Discriminatory Feature Extraction (DFE) method from five different sources: the grayscale image, the YCbCr color image, the 2D Haar wavelet transformed image, the Histograms of Oriented Gradients (HOG), and the Local Binary Patterns (LBP). The DFE method, which applies a new criterion vector defined on two measure vectors, is able to derive multiple discriminatory features in a whitened Principal Component Analysis (PCA) space for two-class problems. As the popular discriminant analysis method derives only one feature for a two-class problem, the DFE method improves upon the discriminant analysis method for two class problems, where one feature usually is not enough for achieving good classification performance. The effectiveness of the DFE method as well as the five types of discriminatory features is evaluated for the eye detection problem. Experiments using the Face Recognition Grand Challenge (FRGC) version 2 database show that the DFE method is able to improve the discriminatory power of the five types of discriminatory features for eye detection. In particular, the experimental results reveal that the discriminatory HOG features achieve the best eye detection performance, followed in order by the discriminatory YCbCr color features, the discriminatory 2D Haar features, the discriminatory grayscale features, and the discriminatory LBP features.