• Bharti Dewangan
• Dr. Minakshi Sonawane

Face-Based Emotion Detection is an important part of Human-Computer Interaction,it is the study and design of how people communicate with computers and digital systems. It,s widely used in many areas like healthcare, security, HCL, marketing, customer behavior studies, and education. This paper gives a clear overview of how FER systems work, the different stages involved,commonly used techniques and datasets, and recent improvements in the field. It covers both traditional machine learning methods deep learning architectures (such as ResNet, VGG, and EfficientNet),IT also discusses popular algorithms such as Convolutional Neural Network(CNN) and Support Vectors Machines(SVM) that are used in building real-time applications.Additionally, it compares how different models performs on well-known datasets like FER-2013,CK+, and AffectNet, focusing on their accuracy, speed, and ability to adapt to different situations. The study also shows how deep learning has improved FER, variety of data, and running in real-time settings.The study focuses on how deep learning techniques have revolutionized facial emotion detection, particularly in recognizing nuanced expressions, dealing with data variability, and supporting live applications.

CNN, deep neural networks, supervised learning, facial emotion detection (including emotions such as happiness, sadness, anger, disgust, fear, surprise, neutrality, and contempt).

"Facial Emotion Recognition using Machine Learning: A Systematic Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.66-73, August-2025, Available :http://www.jetir.org/papers/JETIRHA06011.pdf

"Facial Emotion Recognition using Machine Learning: A Systematic Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp66-73, August-2025, Available at : http://www.jetir.org/papers/JETIRHA06011.pdf