Facial Expression Recognition of Pets Using Deep Learning

Abstract

Understanding the emotional states of pets is a significant aspect of their well-being and effective communication between humans and animals. This research investigates the development of a deep learning-based system for facial expression recognition in pets. Leveraging convolutional neural networks (CNNs) and transfer learning techniques, the proposed model aims to accurately detect and classify diverse facial expressions exhibited by various animal species, such as dogs, cats, and others. The study involves the collection and curation of a comprehensive dataset comprising annotated images of pets displaying different emotional cues such as angry, happy, sad, and other. Preprocessing methods tailored to account for the variability in animal faces are employed to enhance model robustness and generalization. Through extensive experimentation and evaluation, the effectiveness and reliability of the developed framework in recognizing and interpreting pet facial expressions are assessed. EfficientNetB5 is used for transfer learning and the accuracy of the detection is around 87%. The outcomes of this research pave the way for innovative applications in veterinary care, animal behavior analysis, and human-animal interaction, fostering a deeper understanding of pet emotions and improving the quality of relationships between pets and their human companions.