Deep features for assessing smile genuineness from video sequences
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["deep features","deep learning","face analysis","face recognition","facial expression","smile genuineness"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 
deep features; deep learning; face analysis; face recognition; facial expression; smile genuineness