Synthesising frontal face image using elastic net penalty and neighbourhood consistency prior

Traditional frontal face image synthesis based on the ℓ₁-penalty has achieved remarkable success. However, the ℓ₁-penalty on reconstruction coefficients has the drawback of instability when processing high-dimensional data (e.g. a facial image including hundreds of pixels). Moreover, the traditional ℓ₁-penalty-based method requires consistency between the corresponding patches in frontal and profile faces, which is hard to guarantee due to self-occlusion. To overcome the instability problem of the traditional method, an extension of the ℓ₁-penalty-based frontal face synthesis method, which benefits from the nature of the elastic net, is presented. 3 addition, to enhance the aforementioned consistency, a neighbourhood consistency penalty is imposed onto the reconstruction coefficients using the connected neighbour patches of the current patch. Furthermore, to ensure the synthesised result faithfully approximates the ground truth, a sparse neighbour selection strategy is introduced for finding related neighbours adaptively. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art methods in both visual and quantitative comparisons.