Facial Expression Recognition and Image Description Generation in Vietnamese

TL;DR

This paper presents a multi-model approach for facial expression recognition and image description generation in Vietnamese, combining YOLOv5, CNN, VGG16, and LSTM to produce descriptive sentences with improved accuracy.

Contribution

It introduces a novel integrated system that combines facial expression recognition and image captioning tailored for Vietnamese, utilizing YOLOv5 and a merged architecture for enhanced performance.

Findings

YOLOv5 outperforms traditional CNN in emotion recognition with 0.938 accuracy

The image description model achieves BLEU scores up to 0.628 for BLEU-1

Combining models improves the accuracy of visual and emotional content description.

Abstract

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.