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Proceedings ArticleDOI

A Survey on Facial Expression Recognition using Machine Learning Techniques

TLDR
This research provides a broad overview of the FER process includes all stages of FER system as well as the various methods used to evaluate the efficiency of theVarious methods of facial expression recognition.
Abstract
In recent years, many researchers are taking an interest in the research area of Face recognition due to its diverse applications such as security systems, medical systems, entertainment. Nowadays, many kinds of biometric information processing systems are used for various purpose face-recognition systems. The facial expression that can define the human mental state and behavior and it is used for security purpose. FER is used in domains such as healthcare, marketing, environment, safety and social media. This paper presents the survey of the facial expression recognition system that includes the four main stages i.e. face detection, pre-processing, extraction of features, and classification. This research provides a broad overview of the FER process includes all stages of FER system as well as the various methods used to evaluate the efficiency of the various methods of facial expression recognition. This survey paper also helps to understand the approaches, different techniques that address and analyse the problems and challenges comes in the real-time environment. Finally, this paper concludes the state-of-the-art and explore the challenges faced in implementation of FER process along with the scope of future development.

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Citations
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Journal ArticleDOI

Recognition of facial expression of fetuses by artificial intelligence (AI).

TL;DR: In this paper, the development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study is presented.
Journal ArticleDOI

Efficient-SwishNet Based System for Facial Emotion Recognition

- 01 Jan 2022 - 
TL;DR: In this article , a light-weight Efficient-SwishNet model was proposed for emotion recognition that is robust to certain conditions such as variations in illumination, face angles, gender, race, background settings and people belonging to diverse geographical regions.
References
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Journal ArticleDOI

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

TL;DR: A novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered and both the VLBP and LBP-TOP clearly outperformed the earlier approaches.
Journal ArticleDOI

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

TL;DR: This paper presents a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences and introduces facial-action-dynamics recognition from continuous video input using temporal rules.
Journal ArticleDOI

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

TL;DR: A novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts and the experimental results show that the proposed method is highly competitive compared with other state-of-the-art approaches.
Journal ArticleDOI

Static and dynamic 3D facial expression recognition: A comprehensive survey

TL;DR: Developments in 3D facial data acquisition and tracking are discussed, and currently available 3D/4D face databases suitable for 3D and 4D facial expressions analysis are presented as well as the existing facial expression recognition systems that exploit either 3D or 4D data in detail.
Journal ArticleDOI

The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition

TL;DR: An automatic multiclass arousal/valence classifier is implemented comparing performance when extracted features from nonlinear methods are used as an alternative to standard features and results show that, when nonlinearly extracted features are used, the percentages of successful recognition dramatically increase.
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