Single-sample face recognition with image corruption and misalignment via sparse illumination transfer

Single-sample face recognition is one of the most chal-lenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) frame-work. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the miss-ing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) tech-nique is introduced. The SIT algorithms seek additional il-lumination examples of face images from one or more addi-tional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumi-nation information from the alignment stage to the recog-nition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the exist-ing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC al-gorithm using multiple training images; and the face recog-nition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization. 1