Efficient Facial Emotion Detection through Deep Learning Techniques

Abstract

Smart facial emotion detection represents a captivating realm of inquiry that has found applications across diverse sectors such as defense, healthcare, and human-machine interfaces. Researchers are diligently exploring methods to encode, decode, and even obfuscate facial cues to refine algorithmic predictions. Leveraging a combination of deep learning algorithms and Cognitive Internet of Things (CIoT), efforts are underway to bolster efficiency in response to the rapid evolution of this technology. This study aims to distill recent advancements in smart facial expression recognition utilizing deep learning algorithms while pioneering novel approaches to emotion detection. The burgeoning Internet of Things landscape has underscored a deficiency in technological infrastructure within current automated intelligent services, rendering them ill-equipped to cater to industrial demands. The gradual augmentation of Internet of Things technologies tailored for intelligent environments has inadvertently led to delays and diminished market efficacy. Deep learning stands out as a cornerstone in myriad applications and experimental setups. Addressing this challenge necessitates the formulation of emotionally intelligent methodologies within the framework of deep learning, thereby invigorating Internet of Things initiatives, as elucidated by recent strides in facial emotion detection applications.