Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel method to recover images of faces, particularly when large spatial regions of the face are unavailable due to data losses or occlusions. In contrast with previous work,  do not make assumptions on the data neither during training nor testing (such as assuming that the person was seen before or that all faces are perfectly aligned and have identical head pose, expression, etc.). Instead,  propose to tackle the problem in a purely unsupervised way, leveraging a large face dataset. During training, first  cluster faces based on their landmark's positions (obtained by an automatic face landmark estimator). Then,  model the face appearance for each group using sparse coding with learned dictionaries, with one dictionary per cluster. At test time, given a face to recover,  find its belonging cluster and occluded area and restore missing pixels by applying the group-specific sparse appearance representation learned during training.  show results on two "in the wild" datasets. Our method shows promising results on challenging faces and our sparse coding approach outperforms prior subspace learning techniques.
['Xavier P. Burgos-Artizzu', 'Joaquin Zepeda', 'Francois Le Clerc', 'Patrick Pérez']
I am writing a research paper on a idea Pose and Expression-Coherent Face Recovery in the Wild
[{"from": "human", "value": "I am writing a research paper on a idea Pose and Expression-Coherent Face Recovery in the Wild"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel method to recover images of faces, particularly when large spatial regions of the face are unavailable due to data losses or occlusions. In contrast with previous work,  do not make assumptions on the data neither during training nor testing (such as assuming that the person was seen before or that all faces are perfectly aligned and have identical head pose, expression, etc.). Instead,  propose to tackle the problem in a purely unsupervised way, leveraging a large face dataset. During training, first  cluster faces based on their landmark's positions (obtained by an automatic face landmark estimator). Then,  model the face appearance for each group using sparse coding with learned dictionaries, with one dictionary per cluster. At test time, given a face to recover,  find its belonging cluster and occluded area and restore missing pixels by applying the group-specific sparse appearance representation learned during training.  show results on two \"in the wild\" datasets. Our method shows promising results on challenging faces and our sparse coding approach outperforms prior subspace learning techniques."}]