Emotion recognition from facial images, body gestures, and skeletal posture keypoints: The BER2024 dataset

Body language is a crucial aspect of communication, as it helps observers interpret emotional states based on non-verbal cues. However, accurately classifying body language into distinct emotional categories remains a challenging task, particularly due to the limited availability of high-quality datasets focused on various body expressions, particularly in contexts such as the health sector. The computational problem addressed in this study arises from the scarcity of datasets suitable for training classifiers capable of distinguishing four categories of body expressions (negative, neutral, pain, and positive). To address this gap, we introduce the Body Emotion Recognition dataset, specifically designed to provide a robust foundation for training and evaluating body language classifiers. This dataset, created from images of individuals simulating the aforementioned categories, was utilized to test and analyze the performance of three distinct convolutional neural network approaches: one focused on facial images, another on body images, and a third on skeletal posture data represented as keypoints. Transfer learning from models pretrained on ImageNet and other datasets was employed to demonstrate the potential of the proposed dataset in achieving accurate classification of the four body expression categories. Our approach, tested on these models, has achieved varying accuracies depending on the type of data analyzed: a test accuracy of 96.25% when analyzing facial images, 95.59% when analyzing body images, and 67.53% when analyzing skeletal posture data represented as keypoints. These results highlight the quality of the dataset in carefully labeling the images and indicate the amount of information that can be extracted from these categories in the dataset, thus providing a valuable tool for future research in the area of health care.