Synthesising frontal face image using elastic net penalty and neighbourhood consistency prior
Yuanhong Hao, Chun Qi · Electronics Letters · 2015
Traditional frontal face image synthesis based on the ℓ 1 ‐penalty has achieved remarkable success. However, the ℓ 1 ‐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 ℓ 1 ‐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 ℓ 1 ‐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.