Performance Analysis of Feature Extraction Techniques for Facial Expression Recognition

Neha, .; Mathur, Pratistha; Gupta, Sandeep

doi:10.5120/ijca2017912518

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…The geometric-based method extracts feature like edge features and corner features. Neha et al [50] analyzed the performance of the feature extraction technique Gabor filter. They also tested the average gabor filter and compared both the filtering techniques to enhance the recognition rate.…”

Section: Need For Feature Extractionmentioning

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: Need For Feature Extractionmentioning

confidence: 99%

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

et al. 2020

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Query-Based Image Retrieval Using SVM

Janu

Gupta

Nawal

et al. 2022

Communications in Computer and Information Science

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Multiple Watermarking Scheme for Video & Image for Authentication & Copyright Protection

Janu

Kumar

Dadheech

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Watermarking has been used frequently to authenticate the accuracy and security of the image and video files. In the world of computer technology, several watermarking strategies have developed during the past 20 years. Integrate a picture of identity that is not always concealed, such that no detail is not possible to delete. A monitoring code can also be used to deter unauthorized recording equipment. Another application is the watermark copyright control, which works at stopping the creator of the image from stealing photos unlawfully. A watermark is a promising option for the copyright transcendence of multi-media files, as embedded messages are still included. Due to the limits of fidelity, a watermark may be implemented in a small multimedia data space. There is no proof that Automated Watermarking technologies will fulfil the ultimate purpose of the cleaners of all sorts of copyright security operations to gather knowledge from the data they obtain. Relevant situations may be deemed more fairly expected with the usage of automated copyright marking technologies. A perfect device will not be able to add a digital watermark without the limit, which does not supply the whole object with details. In this work, a modern technique for watermarking includes injecting two or more messages or photographs into a single picture for protection purposes and repeating a similar procedure for N-frames for authentication in the film.

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Performance Analysis of Feature Extraction Techniques for Facial Expression Recognition

Cited by 6 publications

References 8 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

Query-Based Image Retrieval Using SVM

Multiple Watermarking Scheme for Video & Image for Authentication & Copyright Protection

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