A hybrid-supervision learning algorithm for real-time un-completed face recognition

It is still an important and challenging problem for face recognition with occlusion, small sample size, various expressions, and poses, called un-completed face recognition. So we design a simple but effective hybrid-supervision learning frame by fusing the advantages of supervised and unsupervised features. In the supervised branch, we propose an effective feature learning method: HMMFA. In the unsupervised branch, we improve the PCANet to extract more effective local information. In the fusion stage, we further extract the discriminant features contained in the hybrid features and then take SVM as the final classifier. Because the proposed method requires no auxiliary set and has less parameter number than that of deep learning methods, it has a low storage requirement, which makes it more economical and practical for small communities. Experiments on four databases show the effectiveness and efficiency of our method.

Automated summary

The study presents a hybrid-supervision learning frame that combines the advantages of supervised and unsupervised features, featuring an effective feature learning method and local information extraction through PCANet, culminating in a SVM final classifier for addressing challenges in face recognition. This method is practical for small communities due to its low storage requirement and has demonstrated effectiveness and efficiency through experiments on four databases.