Deep features for assessing smile genuineness from video sequences

This package contains the deep features extracted from the UvA-NEMO database (https://www.uva-nemo.org/) accompanied with a script (read_bin_file.py) that can be conveniently used for reading these features from the binary files. To extract features from the cropped faces (color images of size 224x224) within each video frame, we exploited the feature extraction part of a VGG-19 convolutional neural network pre-trained on ImageNet (note that the UvA-NEMO images are thus normalized using the mean and standard deviation of ImageNet). For each video sequence, we build a single feature vector with the features extracted from the face regions in all frames, appended one after another. We dump the features from the final layer in the feature extraction part of VGG-19 (after the adaptive average pooling). For each video sequence, we store (in the following order): the number of features, the number of frames, and the height and width of the adaptive average pooling (used after the final convolutional layer), and the features (extracted from the first frame presenting a face, followed by the features extracted from the second frame presenting a face, and so forth). Overall, we have 1240 binary files including the features extracted for 1240 UvA-NEMO sequences manifesting posed and genuine smiles.