Facial expression recognition using geometric features

Durmuşoğlu, Alptekin; Kahraman, Yavuz

doi:10.1109/iwssip.2016.7502700

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Each of the pictures in the database, GLCM highlights are extricated, and the information image is put away each relative performing minutes. For example, the four widely used properties: Heat, Inverse, Entropy, and Contrast distinguishing minute, are utilized to diminish the multifaceted calculation nature [3]. The co-event network is a practical model that is useful in many applications for image research, including biomedical detection, remote detection, modern frameworks for deformity recognition, etc.…”

Section: Terminologymentioning

confidence: 99%

RETRACTED ARTICLE: Emotion recognition model based on facial expressions

Yadav¹

2021

Multimed Tools Appl

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Face mining is characterized as the revelation of picture designs in a given congregation of pictures. It is an exertion that generally attracts upon information PC (Personal Computer) vision, picture handling, information mining, AI (Artificial Intelligence), database, and human-made reasoning. Facial acknowledgement breaks down and contemplates the examples from the images of the facial. Facial component extraction is a programmed acknowledgment of human faces by recognizing its highlights, for example, eyebrows, eyes, and lips. In this paper, we are assessing the execution of PCA (Priniciple Component Analysis), GMM (Gaussian Mixture Models), GLCM (Gray Level Co-Occurrence Matrix), and SVM (Support Vector Machines) to perceive seven distinctive outward appearances of two people, for example, angry, sad, happy, disgust, neutral, fear, and surprise in database. Our point is to talk about the best systems that work best for facial acknowledgement. The present investigation demonstrates the plausibility of outward appearance acknowledgement for viable applications like surveillance and human PC communication.

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Section: Terminologymentioning

confidence: 99%

RETRACTED ARTICLE: Emotion recognition model based on facial expressions

Yadav¹

2021

Multimed Tools Appl

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“…The features in relation to facial regions such as distance between facial components, orientation of facial components, are used in facial feature representation. Geometrical feature extraction demands accurate detection and localization, along with tracking of prominent landmarks on the face [14]. This is particularly challenging to achieve in real‐life scenarios.…”

Section: Related Workmentioning

confidence: 99%

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Kumar¹,

Kumar

et al. 2022

IET Image Processing

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Facial expression is one form of communication which being non‐verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused on global feature extraction assuming that all facial regions contribute equal amount of discriminative information to predict the expression class. The detection and localization of facial regions that have significant contribution to expression recognition and extraction of highly discriminative feature distribution from those regions are not fully explored. The key contributions of the proposed work are developing novel feature distribution upon combining the discriminative power of shape and texture feature; determining the contribution of facial regions and identifying the prominent facial regions that hold abstract and highly discriminative information for expression recognition. The shape and texture features taken into consideration are Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). Multiclass Support Vector Machine (MSVM) is used while one versus one classification. The proposed work is implemented on CK+, KDEF, and JAFFE benchmark facial expression datasets. The recognition rate of the proposed work is 94.2% on CK+ and 93.7% on KDEF, which is significantly more than the existing handcrafted feature‐based methods.

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“…The handcrafted feature‐based AFER can be categorized as either geometric or appearance‐based. The reliable recognition, localization, and tracking of face landmark points introduces significant challenges in the context of geometric feature‐based approaches [3]. The selection of an optimal window size plays a prominent role in appearance feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Modelling appearance variations in expressive and neutral face image for automatic facial expression recognition

Kumar H N,

M S,

Asif Shah

et al. 2024

IET Image Processing

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In automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined integrating multiple features to enhance the recognition rate of AFER systems. However, the feature variations between expressive and neutral face images are not fully explored to identify the expression class. The proposed research presents an innovative approach to AFER by modelling appearance variations in both expressive and neutral face images. The prominent contributions of the work are developing a novel and hybrid feature space by integrating the discriminative feature distribution derived from expressive and neutral face images; preserving the highly discriminative latent feature distribution using autoencoders. Local binary pattern (LBP) and histogram of oriented gradients (HOG) are the feature descriptors employed to derive the discriminative texture and shape information, respectively. The component‐based approach is employed, wherein the features are derived from the salient facial regions instead of the whole face. The three‐stage stacked deep convolutional autoencoder (SDCA) and multi‐class support vector machine (MSVM) are employed to address dimensionality reduction and classification, respectively. The efficacy of the proposed model is substantiated by empirical findings, which establish its superiority in terms of accuracy in AFER tasks on widely recognized benchmark datasets.

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Facial expression recognition using geometric features

Cited by 11 publications

References 21 publications

RETRACTED ARTICLE: Emotion recognition model based on facial expressions

RETRACTED ARTICLE: Emotion recognition model based on facial expressions

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Modelling appearance variations in expressive and neutral face image for automatic facial expression recognition

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