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Human emotion recognition is an important direction in the field of biometricand information forensics. However, most existing human emotion research arebased on the single RGB view. In this paper, we introduce a RGBD video-emotiondataset and a RGBD face-emotion dataset for research. To our best knowledge,this may be the first RGBD video-emotion dataset. We propose a new supervisednonlinear multi-view laplacian eigenmaps (MvLE) approach and amultihidden-layer out-of-sample network (MHON) for RGB-D humanemotionrecognition. To get better representations of RGB view and depth view, MvLE isused to map the training set of both views from original space into the commonsubspace. As RGB view and depth view lie in different spaces, a new distancemetric bag of neighbors (BON) used in MvLE can get the similar distributions ofthe two views. Finally, MHON is used to get the low-dimensional representationsof test data and predict their labels. MvLE can deal with the cases that RGBview and depth view have different size of features, even different number ofsamples and classes. And our methods can be easily extended to more than twoviews. The experiment results indicate the effectiveness of our methods oversome state-of-art methods.

Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition