Smart Low-Light Image Enhancement For Emotion Detection

Facial expression-based emotion recognition has emerged as a key element of contemporary human-computer interaction (HCI), with uses in smart environments, entertainment, security, and healthcare. However, as bad lighting deteriorates image quality and blurs facial characteristics, low light levels in real-world situations provide serious obstacles to successful emotion recognition. In order to solve this problem, this research suggests a Smart Low-Light Image Enhancement for an Emotion Detection system that maximizes performance in low-illumination environments by fusing deep learning-based emotion detection models with sophisticated image preprocessing approaches. In order to improve image brightness, contrast, and noise reduction, the suggested system incorporates a number of image enhancement techniques, such as histogram equalization, Retinex algorithms, and deep learning-based models like convolutional neural networks (CNNs) and generative adversarial networks (GANs). After improvement, pre-trained models for detecting emotions such as ResNet, VGGFace, and bespoke CNN architectures—are used to categorise emotions such as fear, rage, sadness, and happiness. To ensure ideal quality for dependable emotion detection, an adaptive feedback loop is used to re-enhance images when the emotion recognition accuracy drops below a predetermined level. This system is ideal for use in smart home technologies, driver monitoring, mental health evaluation, and surveillance systems because it is made for real-time processing and delivers excellent accuracy with little computing overhead. According to experimental results, combining emotion identification models with smart picture enhancement greatly increases accuracy in low light, offering a reliable option for emotion-aware systems in demanding and dynamic contexts. By improving the accuracy of emotion detection, this work advances the field of HCI and promotes more sympathetic, context-aware interactions across a range of applications.