The human face information, like the fingerprint and iris, can be used for the identification of a person, and is easier to achieve long-range resolution and identification. Hyperspectral imaging can be used to obtain a wealth of chemical properties and a large amount of data for human identity, while we need to use chemometric methods to extract the chemical characteristics from the image dataset, and the obtained face feature can be used for computer recognition. In this paper, we have investigated the feasibility of the analysis of human face by using hyperspectral imaging combined with chemometric methods. We compared the results of multivariate curve resolution alternating least squares (MCR-ALS) and principal component analysis (PCA). MCR-ALS gave the pure principal components spectra and their corresponding relative concentrations to display the information of the human face, and constraints could be applied conveniently based the features of imaging data. In addition, the method of partial least squares discriminant analysis (PLS-DA) was used to classify the human skin signal spectrum. From the spectra analyzed, the facial information of white and yellow people is similar, and the classification of them are more difficult than that of dark skinned people.