Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

Abstract

We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing virtual reality headsets. On the other hand, we aim to estimate the age and identify the gender of people wearing surgical masks. For all these tasks, the common ground is that half of the face is occluded. In this challenging setting, we show that convolutional neural networks trained on fully visible faces exhibit very low performance levels. While fine-tuning the deep learning models on occluded faces is extremely useful, we show that additional performance gains can be obtained by distilling knowledge from models trained on fully visible faces. To this end, we study two knowledge distillation methods, one based on teacher–student training and one based on triplet loss. Our main contribution consists in a novel approach for knowledge distillation based on triplet loss, which generalizes across models and tasks. Furthermore, we consider combining distilled models learned through conventional teacher–student training or through our novel teacher–student training based on triplet loss. We provide empirical evidence showing that, in most cases, both individual and combined knowledge distillation methods bring statistically significant performance improvements. We conduct experiments with three different neural models (VGG-f, VGG-face and ResNet-50) on various tasks (facial expression recognition, gender recognition, age estimation), showing consistent improvements regardless of the model or task.

Recognition of Student Emotions in Classroom Learning Based on Image Processing

“…In the process of learning, especially online learning, students should keep positive emotions like delightfulness, cheerfulness, joy, and enthusiasm, and try to realize happy learning. This would improve learning efficiency and learning effect [16][17][18][19][20]. Therefore, monitoring student emotions in classroom learning is an important means to assist teachers in online teaching, and classroom learning emotions directly affect teaching and learning effects [21][22][23].…”

Sweeping the social minefield : detecting online opinions and emotions with NLP

“…Triplet loss has been used successfully in various approaches for emotion detection in images (Georgescu et al, 2022;Haider et al, 2023), audio data (Ren et al, 2019;Kumar et al, 2021), and multi-modal data. Chudasama et al (2022), for example, propose M2FNet: a multi-modal fusion network for emotion detection in conversations.…”