Human Mood Detection using Image Processing and Machine Learning and Deep Learning
Aloke Lal Barnwal1, Rupashri Barik2
1Mr. Aloke Lal Barnwal, JIS College of Engineering, Kalyani (West Bengal), India.
2Ms. Rupashri Barik, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.
Manuscript received on 15 July 2023 | First Revised Manuscript received on 27 October 2024 | Second Revised Manuscript received on 05 January 2025 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025 | PP: 28-31 | Volume-14 Issue-6, January 2025 | Retrieval Number: 100.1/ijsce.I97000812923 | DOI: 10.35940/ijsce.I9700.14060125
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This work aims to develop an efficient algorithm to automatic the detection of emotions based on facial expressions. Which involves the use of computer vision & machine learning techniques to classify the emotions of individuals or groupsin realtime using an image. As mood detection refers to the process of using various techniques and tools to identify or recognize the emotional state or mood of an individual based on their facial expressions. Its purpose will be to provide insights into the psychological state of individuals for various applications such as mental health diagnosis. It typically involves the use of machine learning algorithms and natural language processing techniques to analyze and interpret human behavior. This approach also uses deep learning models to learn the features of facial expressions and detect the corresponding emotions. The results show that the proposed algorithm is accurately detecting emotions from images with better accuracy & less false detection, which can be suitable for use in various applications such as healthcare, entertainment and social media.
Keywords: Haar Cascade, Convolution on Image, Deep Face, LBP.
Scope of the Article: Image Processing and Recognition