• Priyamvada Sharma
• Neha Bhardwaj

How a person seems to be feeling In interpersonal communication, one of the most difficult and vital skills to master is the ability to give and receive an acknowledgement. It is easy to tell how people feel and what they aim to accomplish by their facial expressions. Nonverbal communication relies heavily on facial expressions. Automated facial expression identification is becoming more dependent on deep neural networks. In part, this is because FER has gone from lab-controlled to real-world situations, where deep learning methods have proven useful in a variety of industries. Two major issues have been addressed in recent deep FER systems: overfitting, which occurs when there isn't enough training data, and elements that don't have anything to do with the expression of the subject, such as illumination, and head position, and identification bias. This study provides an in-depth examination of deep FER, which includes datasets and approaches that shed light on the issues at hand. To begin, we'll go through the datasets that the general public has access to. These datasets have been extensively studied in the scientific literature, and a variety of data selection and assessment techniques have been used. This is followed by an explanation of the standard deep FER system pipeline, as well as background information and recommendations for successful implementations at each level. For deep FER, we look at the most cutting-edge deep neural networks and training approaches for FER based on static photographs and dynamic image sequences, as well as advantages and drawbacks. Other commonly used benchmarks are included in this section as well. Then, in order to make our poll even more helpful, we add more topics and purposes to it. Last but not least, we examine the challenges and opportunities that remain in this sector and how to construct robust deep FER systems in the near future.

FER, CNN, Machine Learning, Facial Expression, geometry-based Feature Extraction.

" A Review of Facial Expression Recognition using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.h530-h537, June-2022, Available :http://www.jetir.org/papers/JETIR2206757.pdf

" A Review of Facial Expression Recognition using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pph530-h537, June-2022, Available at : http://www.jetir.org/papers/JETIR2206757.pdf