An Efficient Expression Recognition Method using Contourlet Transform

Biswas, Suparna; Sil, Jaya

doi:10.1145/2708463.2709036

Cited by 15 publications

(4 citation statements)

References 15 publications

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“…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]. support vector machine (SVM) is another classification method involving two types of techniques: one against one and one against all [45]- [47].…”

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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“…In the LP stage, it splits the image into the low pass, and band-pass, and confines the position of the discontinuities. The DFB stage handles band passing and shapes the linear structure by correlating the position of discontinuities [16].…”

Section: Discrete Contourlet Transform (Dct)mentioning

confidence: 99%

Face Recognition Algorithms: A Review

Khorsheed

Nayef

2022

ACAD J NAWROZ UNIV

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Facial recognition is the method by which an individual's identity is determined by a facial image. With the support of this method, it is potential to use the face image of the person to document it in any safety system. Facial recognition methods for static images can approximately be classified into comprehensive approaches to comprehensive and feature-based approaches. The comprehensive systems use the whole raw face image as input, while feature-based approaches utilize limited face features and use their regular and decorative properties [1]. A huge number of facial recognition systems have been advanced in the past periods. In this paper, we review a varied range of approaches used to identify facial recognition, which contains LDA, PCA, SVM, and ICA.

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“…For the reduction of the face image size, the Bessel down sample has been used, but somehow it preserves features as well as the perceptive nature of the original image [6]. The Gaussian filter is often used to resize input images to give the images sharpness [7]. Normalization is the pre-processing technique that can be meant to reduce light and change dramatically facial images [8] using the median filter and to change the perception of the face.…”

Section: Fig 1 Facial Expressions Presented To Users [1]mentioning

confidence: 99%

Performance Improving of ANN with Preprocessing Stage in Human Face Expression Recognition System

H.¹,

Vasuki²

2020

IJITEE

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In many face recognition systems, the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position, and orientation. So, by using various modeling techniques it is convenient to recognize various facial expressions. The system proposed consists of three phases, the facial expression database, pre-processing and classification. To simulate and assess recognition efficiency based on different variables (network composition, learning patterns and pre-processing), we present both the Japanese Female Facial Expression Database (JAFFE) and the Extended Cohn-Kanade Dataset (CK+). Comparative approaches of data preprocessing include face detection, translation, normalization of global contrast and histogram equalization. Significant results were obtained with 85.52 percent accuracy particularly in comparison with some other pre-processing phases and raw data in single pre-processing phases. The result indicates the ANN classifier representation produces a satisfactory result which reaches more accuracy.

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An Efficient Expression Recognition Method using Contourlet Transform

Cited by 15 publications

References 15 publications

Investigation into facial expression recognition methods: a review

Investigation into facial expression recognition methods: a review

Face Recognition Algorithms: A Review

Performance Improving of ANN with Preprocessing Stage in Human Face Expression Recognition System

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