Facial Emotion Detection and Its Applications - Sameer Awasthi; Abhishant Verma; Abhishek Singh; Raghuveer Prasad; Praveen Kumar (International Journal of Optical Innovations & Research, 2025)
Facial emotion detection (FED) is an interdisciplinary field that integrates artificial intelligence, computer vision, and machine learning to recognize and interpret human emotions based on facial expressions. The development of FED systems has been propelled by advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which enhance recognition accuracy. Feature extraction techniques, including geometric and appearance-based methods, play a crucial role in classifying emotional states. FED has diverse applications across multiple domains. In healthcare, it aids in diagnosing mental health conditions, supporting therapy, and assisting individuals with autism in recognizing emotions. In human-computer interaction (HCI), FED enhances virtual assistants, improves gaming experiences, and enables emotion-aware robotics. Security and surveillance benefit from FED by detecting suspicious behaviors and augmenting lie detection. In marketing, customer feedback can be analyzed to improve user experience and advertisement targeting. The education sector utilizes FED for monitoring student engagement and adapting learning experiences to emotional states. Despite its vast potential, FED faces several challenges, including variations in facial expressions due to age, ethnicity, and cultural backgrounds. Environmental factors such as lighting and occlusions also impact accuracy. Ethical concerns surrounding data privacy, bias in facial recognition models, and potential misuse necessitate responsible AI development. Future research should focus on improving model robustness, real-time performance, and privacy-preserving techniques such as federated learning and encryption. FED continues to evolve, offering significant improvements in human-computer interaction and affective computing. Addressing the existing challenges will pave the way for broader adoption and enhanced reliability of emotion recognition systems in real-world applications.
Keywords: Computer vision; Deep Learning; Facial emotion detection; affective computing; human-computer interaction