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A novel model for Two-Dimensional Independent TV- Based Quotient Image is proposed in this paper, and the new proposed model is applied to face recognition with only one sample per subject on a large scale face database. In our model, an ETVQI model is firstly proposed to overcome drawbacks of the TVQI model. Then, a two-dimensional independent subspace analysis algorithm is proposed to reduce the dimension of face samples normalized by ETVQI model and to extract high-order statistical features of these samples. According to experiments on the CAS-PEAL face database, our proposed model could outperform ICA with architecture II (ICA2), (2D)2PCA and TVQI. And it also confirms that our model is robust not only to illumination, but to other outliers (expression, masking, occlusion etc.) in face recognition.