EmoTrack: Intelligent Hiring with Data and Emotion Detection

This paper presents EmoTrack: an advanced emotion detection system designed to monitor and analyze facial expressions during interviews. The system employs computer vision and machine learning techniques to detect emotions such as happiness, sadness, disgust, anger, neutrality, and surprise. Interviews are a critical component of hiring and candidate assessment, yet they are often influenced by human biases and subjectivity. EmoTrack addresses this issue by offering a data-driven approach to emotional analysis, improving decision-making through objective insights. By capturing real-time facial expressions and processing them using deep learning models, the system generates comprehensive emotional profiles that enhance the interview evaluation process. The implementation includes a video capture module, feature extraction using OpenCV, emotion classification through convolutional neural networks (CNNs), and temporal analysis to track emotional shifts throughout the interview. Additionally, the system compiles emotional insights into structured reports, making it easier for interviewers to interpret emotional data. Through controlled experiments, EmoTrack has demonstrated promising accuracy levels, highlighting its potential applications beyond hiring, such as in psychology, education, and marketing research. This paper discusses the system design, experimental validation, findings, and future enhancements. The results indicate that EmoTrack is a robust tool for emotion analysis in high-stakes settings and can contribute significantly to unbiased and effective decision-making.