Transparent Peer Review By Scholar9
Deep Learning Model to Detect and Classify Emotions of Facial Expressions
Abstract
In recent years’ service robotics has improved their performance, there will surely be a revolution with service robotics in the coming years like the one that occurred with industrial robotics. But before, it is necessary that robots, especially humanoids, perceive our emotions to be able to adapt to our needs. Artificial intelligence has had increasing involvement in any scope of hu- man life. The technologies are adapted to the needs of the human being and artificial intelligence is what makes this adaptation between technology and humans possible. These techniques are used in algorithms for the recognition of human emotions. When humans try to communicate with other people a very high percentage is represented by non-verbal communication. Many studies show that facial expressions have a connection with human emotions. The ability of human beings to detect and identify these emotions makes it possible for us to understand each other. The main objective of this part of artificial intelligence is to use learning techniques in order to get the machine capable of identifying these emotions. Machine learning and deep learning are being used for emotion recognition. This work presents a comparison of current state-of-the-art learning strategies that can handle data and adapt classical static approaches to deal with images sequence. Machine learning algorithms and deep learning versions of CNN are evaluated and compared using different datasets for universal emotion recognition, where the performances are shown, and the pros and cons are discussed.
Shreyas Mahimkar Reviewer
17 Sep 2024 04:03 PM
Approved
Relevance and Originality
The Research Article is highly relevant as it addresses the growing field of service robotics and the necessity for emotional recognition capabilities in humanoid robots. The focus on integrating artificial intelligence (AI) for emotion recognition is original and timely, given the increasing role of AI in various aspects of human life. By evaluating state-of-the-art machine learning and deep learning techniques for this purpose, the article contributes novel insights into enhancing human-robot interaction through emotional understanding.
Methodology
The Research Article outlines a methodology that involves comparing various machine learning and deep learning strategies for emotion recognition. It highlights the use of state-of-the-art algorithms and datasets to evaluate performance. However, the paper could benefit from a more detailed explanation of the specific algorithms and datasets used, as well as the criteria for evaluating their effectiveness. Including a clear description of the experimental setup and any preprocessing steps would improve the methodological clarity.
Validity & Reliability
The validity of the Research Article is supported by its comparative analysis of different learning strategies and the use of diverse datasets for emotion recognition. To ensure reliability, the paper should provide details on how the datasets were selected and the statistical methods used to assess performance. Including information on potential sources of bias and the consistency of results across different datasets would further enhance the reliability of the findings.
Clarity and Structure
The Research Article is generally clear in its discussion of AI and emotion recognition but could be improved in terms of structure. The paper would benefit from clearly defined sections on methodology, results, and discussions to better guide the reader through the complex information. Ensuring that each section is well-organized and that the connections between different parts of the study are explicitly stated would improve overall readability.
Result Analysis
The Research Article provides a comparative analysis of machine learning and deep learning techniques for emotion recognition. It discusses the performance of various algorithms and their applicability to emotion detection. For a more comprehensive result analysis, the paper should present detailed quantitative results and highlight how the proposed techniques outperform or complement existing methods. A deeper discussion on the implications of these results for practical applications in service robotics would also add value.
IJ Publication Publisher
Done Sir
Shreyas Mahimkar Reviewer