Facial Expression Recognition and Image Description Generation in Vietnamese
This work addresses image understanding and emotion analysis for Vietnamese language applications, but it is incremental as it combines existing methods like YOLOv5 and VGG16-LSTM without introducing new paradigms.
This paper tackles facial expression recognition and image description generation in Vietnamese by comparing YOLOv5 and CNN models on the KDEF dataset, with YOLOv5 achieving 0.938 accuracy versus 0.853 for CNN, and proposes a merged VGG16-LSTM architecture for description generation, achieving BLEU scores up to 0.628 on the Flickr8k dataset.
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.