Unsupervised joint face alignment with gradient correlation coefficient
Résumé
This work proposes an unsupervised jointalignment framework, referred to as ‘‘Gradient CorrelationCongealing,’’ which aligns an image ensemble bymaximizing a sum of gradient correlation coefficientfunction defined over all images. We, respectively, developtwo different formulations to optimize the objective functionregarding the role of ‘‘template.’’ While most existingface alignment methods suffer from outliers, e.g., occlusions,the proposed algorithms are able to align faces undergoingpartial occlusions. Moreover, our algorithms cancope with nonuniform illumination changes (even extremelydifficult ones), and also, they do not require anypredefined templates. We test the novel approaches againstfour typical joint alignment methods including Least-Squares Congealing, Learned-Miller Congealing, Lucas–Kanade entropy Congealing, and RASL using three challengingface databases: AR, Yale B, and LFW. Experimentalresults prove the efficiency of our approachesunder different conditions, especially when faces are partiallyoccluded, and the proposed algorithms perform muchbetter than all considered methods