• Ms. Sunaina Rajoriya
• Mr. Rajneesh Pachouri
• Mr. Anurag Jain

In recent years, the integration of artificial intelligence (AI) and machine learning techniques has shown remarkable potential in various domains, including mental health assessment. This project presents a novel approach titled "Artificial Intelligence based Facial Emotions Analysis for Depression Detection," aimed at leveraging AI and deep learning to detect and classify depression levels based on facial emotion analysis. The project utilizes the Matlab programming environment and employs the AlexNet Convolution Neural Network (CNN) model for accurate emotion recognition. The primary objective of this research is to create a robust system capable of recognizing five primary emotions—Anger, Disgust, Happy, Neutral, and Sadness—by analyzing facial expressions in images. These emotions serve as vital indicators for assessing an individual's mental state, particularly when it comes to depression detection. The developed system not only identifies emotions but also classifies depression into four distinct levels: No Depression, Mild Depression, Moderate Depression, and High Depression. This multi-class classification enables a more nuanced understanding of the individual's mental health status. To achieve high accuracy in emotion recognition and depression classification, the AlexNet CNN model is employed. This model is renowned for its deep architecture and remarkable feature extraction capabilities, making it an ideal choice for complex image analysis tasks. Through an extensive training process using a diverse dataset containing facial expressions of various intensities, the system attains an impressive accuracy rate of 99%.The project's contributions are twofold. Firstly, it provides a reliable method for automatically analyzing facial emotions, eliminating the subjectivity inherent in traditional assessment methods. Secondly, the integration of AI and deep learning with mental health assessment opens up new possibilities for early depression detection and intervention.

Deep leaning, Artificial intelligence, Facial Expression, Machine learning, Viola Jones, depression detection, AlexNet CNN contains. -

"Detecting Depression through Facial Emotion Analysis: A CNN and Viola-Jones Algorithm Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d797-d808, May-2024, Available :http://www.jetir.org/papers/JETIR2405392.pdf

"Detecting Depression through Facial Emotion Analysis: A CNN and Viola-Jones Algorithm Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd797-d808, May-2024, Available at : http://www.jetir.org/papers/JETIR2405392.pdf