With the optical sensing technology development, the hyperspectral camera has decreased their price significantly and obtained better resolution and quality. Hyperspectral imaging, recording intrinsic spectral information of the skin at different spectral bands, become a good issue for high performance face recognition. However, there are also many new challenges for hyperspectral face recognition, such as high data dimensionality, low signal to noise ratio and inter band misalignment. This paper proposes a hyperspectral face recognition method based on the covariance fusion of spatio-spectral information and local binary pattern (LBP). Firstly, a cube is slid over the hyperspectral face cube, and each cube is rearranged into a two-dimensional matrix for each overlapping window. Secondly, covariance matrix of each two-dimensional matrix is computed to fully incorporate local spatial information and spectral feature. Thirdly, the trace of each covariance matrix is calculated to replace the pixel values of the fusion image in the corresponding location respectively. Finally, LBP is applied for the fusion hyperspectral face image to get final recognition result. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (90.8%) and lower computational complexity than the state of the art hyperspectral face recognition algorithms.