Sparse frontal face image synthesis from an arbitrary profile image

Frontal face image synthesis from an arbitrary profile image plays an important role in automatic video surveillance systems, and remains a challenge in computer vision. The strategies of partition are popular and promising for synthesizing frontal face images. However, conventional rectangular partition criterions fail to align corresponding patches in profile images and frontal face images. Given an arbitrary profile image, to synthesize a corresponding frontal face image which is smooth in texture and similar in appearance, we introduce a triangulation-based partition criterion and do synthesis based on sparse representation. The triangulation-based partition ensures the corresponding triangular patches are strictly aligned. And sparse representation adaptively finds the most similar patches for synthesis while abandons unlike patches. Furthermore, a confederate learning strategy is proposed to reduce the blocking artifacts caused by triangulation-based partition. Experimental results conducted on the Oriental Face database demonstrate the effectiveness of the proposed frontal face image synthesis method and advantages over previous works.