Facial Expression Classification Using Machine Learning Approach: A Review

Baskar, A.; Kumar, T. Gireesh

doi:10.1007/978-981-10-3223-3_32

Cited by 23 publications

(2 citation statements)

References 14 publications

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“…They failed to compare the advanced DL approach, which is currently the most novel approach in FER. Again, Baskar and Kumar [18] also lacks in explaining various DL approaches. Recently, DL-based FER approaches has been explored in [7], [27], which are the detailed surveys without the discussion on FER.…”

Section: B Scope Of the Surveymentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system. INDEX TERMS Facial sentiment analysis, machine learning, deep learning, convolutional neural network, deep belief network, artificial intelligence.

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Section: B Scope Of the Surveymentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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“…General awareness of some of the major machine learning algorithms has been done on the domain [5]. Like various implementations in the area [6], implementation of various classification methods was also considered [7]. K-Means clustering [8] was a method widely used in segmentation.…”

Section: Introductionmentioning

confidence: 99%

RFM-Based Customer Analysis and Product Recommendation System

Krishnan

Nair

2021

Advanced Computing and Intelligent Technologies

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In this current Covid-19 pandemic scenario, most of the supermarkets and retail stores that are present even in small towns have started giving out products online. This system classifies the customers into categories based on their Recency, Frequency and Monetary (RFM) values and recommends products to them to make sure the potentially valuable customers are retained. Based on the RFM scores, each of these customers will be divided into segments based on the scores that they obtain. To make sure that the customers are retained, better offers should be given to them, and they must also get proper product recommendations. The product recommendation system that is used is based on a concept called cognitive similarity. It is a hybrid recommendation model by taking Collaborative filtering as the base model. Comparison is also done with the existing methods to make sure that there is an improvement in the method put up. For performance evaluation, precision@k was considered. The proposed method using cognitive similarity shows an improvement of 5% in comparison with the existing methods.

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Emotion AI

Sowmya,

Devi

2023

Lecture Notes in Networks and Systems

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Facial Expression Classification Using Machine Learning Approach: A Review

Cited by 23 publications

References 14 publications

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

RFM-Based Customer Analysis and Product Recommendation System

Emotion AI

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