Nowadays, as artificial intelligence continues to advance, face expression recognition has grown in popularity. In interaction technology, emotion recognition is crucial. In interaction technology, nonverbal elements make up two-thirds of communication whereas verbal elements only account for one third. Face expressions are recognized using the Facial Emotion Recognition (FER) technique. The way a person displays his or her inner sentiments, mental state, and human perspective through facial expression is very important. Convolutional neural networks (CNN) classifiers make up the existing system for this project, which has various drawbacks including limited accuracy, a small dataset, and restricted flexibility. The Proposed System is Deep Neural Network (DNN) for feature learning, which has demonstrated success in tackling complex problems, to overcome these limitations Many studies have been done on the application of deep neural networks to face recognition, and many achievements have been reached. Deep Neural Network (DNN) through feature learning performs data representation well and has gained numerous victories in learning and complicated tasks. This study uses a combination of age estimates and gender categorization to detect fundamental human emotions along with object detection. Basic emotions include happy, sad, angry, scared, surprised, and neutral facial expressions. We have selected the FER-2013 and COCO major datasets. With the help of suitable assessment criteria, including precision, recall, F1 score, and accuracy, the success of this face and object analysis project is assessed