Facial Expression Recognition Using 2DPCA on Segmented Images

Islam, Dewan Imdadul; Anal, S R Ngamwal; Datta, Asoke Kumar

doi:10.1007/978-981-10-8237-5_28

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Classifiers categorise expressions including disgust, smile, fear, sad, anger, neutral, and surprise throughout the feature extraction process, which is regarded as the last phase in FER [40], [41]. Several classification methods are utilised in this phase, including the minimum distance classifier (MDC), a distance-based classifier that is used to calculate the distance between two feature vectors, and the dual local histogram descriptor (dLHD) [42], [43], which is used to recognise expressions. The K-nearest neighbours (KNN) approach is also used for classification, where the training phase involves estimating the relationship between the evaluation models [44].…”

Section: Classificationmentioning

confidence: 99%

Investigation into facial expression recognition methods: a review

Devarapalli,

Gonda

2023

IJEECS

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Facial expression recognition (FER) is a rapidly emerging topic in computer vision that has gotten a lot of interest because of its numerous applications in fields including psychology, sociology, human-computer interaction (HCI), and security. FER seeks to recognise and analyse human facial expressions in order to determine emotions and other mental states. Several strategies, including feature-based, kernel-based, and deep learning-based methods, have been developed and implemented in FER in recent years. FER’s major goal is to extract and identify the most discriminating elements that accurately represent the emotions expressed by facial expressions. The literature reviewed in this field shows that deep learning-based methods have outperformed traditional feature-based and kernel-based methods in terms of accuracy and robustness in recognizing facial expressions. However, these deep learning-based methods also pose several challenges, such as the need for large labeled-data-sets, robustness to different facial poses and illumination conditions, and generalization to unseen data. Despite these challenges, the field of FER is expected to continue growing, and future research will likely focus on addressing these challenges and improving the accuracy and robustness of FER systems.

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Section: Classificationmentioning

confidence: 99%

Investigation into facial expression recognition methods: a review

Devarapalli,

Gonda

2023

IJEECS

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“…Also, 2dPCA was used [29]. Other researchers [30] used the LBP as a feature extraction method in FER.…”

Section: Related Workmentioning

confidence: 99%

Facial expression recognition using three-stage support vector machines

Dagher

Dahdah

Shakik

2019

Vis. Comput. Ind. Biomed. Art

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Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.

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“…However, one shortcoming of 2DPCA is that it uses more feature coefficients than the classical PCA for representing image content [10]. Since it was first introduced, 2DPCA has been widely reported as an effective, yet simple, technique for FR [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Human Face Recognition Using Class-wise Two-dimensional Principal Component Analysis

Alsaqre¹

2020

IJCDS

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Two-dimensional principal component analysis (2DPCA) and its variants have been successfully used for the task of face recognition (FR). However, one of the major limitations of 2DPCA-based FR methods is that they only consider the holistic information of a given training dataset, ignoring both class-specific discriminant information and class-separation components, which could further improve recognition performance. To address this limitation, this paper suggests a class-wise 2DPCA (CW2DPCA) framework that seeks to model class-specific subspaces, where each subspace retains the discriminatory information of a particular class, as well as class separability information. In this way, CW2DPCA not only feeds discriminative representations of facial images to the classification model, but also enables a high degree of separation between the different classes present in the training dataset. Experimental evaluation on two face datasets proved the effectiveness of the proposed CW2DPCA in FR.

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Facial Expression Recognition Using 2DPCA on Segmented Images

Cited by 7 publications

References 6 publications

Investigation into facial expression recognition methods: a review

Investigation into facial expression recognition methods: a review

Facial expression recognition using three-stage support vector machines

Human Face Recognition Using Class-wise Two-dimensional Principal Component Analysis

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