From artificial intelligence and gaming to Human-Computer Interaction (HCI) and psychology, facial expression recognition is a crucial research area in most of these disciplines. In this study, a hybrid facial expression recognition model that uses both the Deep Convolutional Neural Network (DCNN) and the FER dataset is proposed. The goal is to group live and digital face images into one of the seven different categories of facial emotion. To improve face feature extraction and filtering depth, the DCNN used in this study comprises more convolutional layers, activation functions, and numerous kernels. A Load haar cascade model was additionally used in conjunction with real-time pictures and video frames to detect facial features. Images from the FER dataset from the Kaggle repository were used, and the training and validation processes were accelerated by taking advantage of Graphics Processing Unit (GPU) processing. Techniques for pre-processing and data augmentation are used to boost classification performance and training effectiveness. The authors may create a real-time schema that can readily match the model and sense emotions thanks to our method's ability to converge quickly and produce good performance. Additionally, this study employs behavioural features to focus on a person's mental or emotional state, which can help human resource managers spot emotional engagement within their workforce. Comparing the experimental results to state-of-the-art (SoTA) trials and research, they reveal a much better categorization performance. This study demonstrates the performance of the suggested design while also demonstrating the significance of its implementation in real life. The future effects of emotion recognition technology on human resources (HR) practises are examined in this paper. Tools for emotion recognition are being used more frequently as AI and machine learning develop. While these tools have the potential to revolutionise HR by offering fresh ways to gauge employee satisfaction and engagement, they also raise significant privacy and ethical issues. The challenges and opportunities presented by this cutting-edge technology are covered in this paper, along with an overview of recent research on emotion recognition and its potential applications in HR. Emotions have an impact on decisions. Emotional measurement has enormous research applications. Face recognition software of today can recognise common facial expressions like happiness, fear, rage, and sadness. It's fascinating to see how emotion analysis and recognition are used in human resources. Think about your hiring and testing options. One such is X0PA Ai, which powers its analytics and video interviewing capabilities using Microsoft's Video Indexer. Using video and audio models, it derives profound insights, including emotional analysis. The method currently being used to gauge emotions is self-report. The self-report method could lead to inaccurate results because employees can easily manipulate the data to software is a useful tool.

Deep Learning, DCNN, Facial Emotion Recognition, Human-Computer Interaction, Haar Cascade, Computer Vision, artificial intelligence (AI)