  This paper discusses a facial expression recognition model and a description
generation model to build descriptive sentences for images and facial
expressions of people in images. Our study shows that YOLOv5 achieves better
results than a traditional CNN for all emotions on the KDEF dataset. In
particular, the accuracies of the CNN and YOLOv5 models for emotion recognition
are 0.853 and 0.938, respectively. A model for generating descriptions for
images based on a merged architecture is proposed using VGG16 with the
descriptions encoded over an LSTM model. YOLOv5 is also used to recognize
dominant colors of objects in the images and correct the color words in the
descriptions generated if it is necessary. If the description contains words
referring to a person, we recognize the emotion of the person in the image.
Finally, we combine the results of all models to create sentences that describe
the visual content and the human emotions in the images. Experimental results
on the Flickr8k dataset in Vietnamese achieve BLEU-1, BLEU-2, BLEU-3, BLEU-4
scores of 0.628; 0.425; 0.280; and 0.174, respectively.
