A novel approach for facial expression recognition based on Gabor filters and genetic algorithm

Boughida, Adil; Kouahla, Mohamed Nadjib; Lafifi, Yacine

doi:10.1007/s12530-021-09393-2

Cited by 43 publications

(14 citation statements)

References 59 publications

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“…Not only the overall silhouette but also the subtle muscle changes and local feature extraction of texture analysis are important focuses when it comes to the changes in human facial expressions. Nagaral et al [16] fused high-order joint derivative local binary pattern (HJDLBP) and local binary pattern (LBP) histogram algorithms for expression recognition, Boughida et al [17] also obtained good results in extracting Gabor features from the detected face region of interest, Eng et al [18] used the ability of HOG to preserve local information and directional density distribution in facial images to extract facial expression features effectively; Besides, Yaddaden et al [19] designed an efficient facial expression recognition method by combining the advantages of HOG and LBP.…”

Section: Related Workmentioning

confidence: 99%

“…[16] fused high‐order joint derivative local binary pattern (HJDLBP) and local binary pattern (LBP) histogram algorithms for expression recognition, Boughida et al. [17] also obtained good results in extracting Gabor features from the detected face region of interest, Eng et al. [18] used the ability of HOG to preserve local information and directional density distribution in facial images to extract facial expression features effectively; Besides, Yaddaden et al.…”

Section: Related Workmentioning

confidence: 99%

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A high‐performance and lightweight framework for real‐time facial expression recognition

Liu

Cao

et al. 2023

IET Image Processing

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Facial expression recognition technology has become a powerful tool for conveying human emotions and intentions and is widely used in areas such as assisted driving and intelligent medical care. Due to the limited computing power of current hardware devices and the real‐time requirements of application scenarios, this paper proposes a high‐performance and lightweight framework for real‐time facial expression recognition framework to solve the problem of real‐time completion of expression recognition tasks under low hardware costs. To address these issues, this paper first designs a RepVGG and mobileNetV2 dual‐channel structure in the feature extraction. It is then input into the MobileViT Block for global feature modelling. Finally, the position vector of the capsule network is used to replace the output of the global pooling, preserving the spatial relationship of the salient features and enhancing the classification effect. Compared with the mainstream facial expression recognition algorithm that cannot get good classification results under low complexity conditions, the model has a significant accuracy improvement while ensuring lightweight. With only 294.60M FLOPS and 0.95M parameters, it achieved an accuracy of 97.53% on the KDEF dataset and 85.56% on the RAF‐DB, demonstrating the advanced nature of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A high‐performance and lightweight framework for real‐time facial expression recognition

Liu

Cao

et al. 2023

IET Image Processing

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“…According to [28], [29] it can be concluded that the Gabor descriptor is effective for horse face characterization. Gabor descriptor has proven its sufficiency in many recent works for face recognition [2], [14], [38], [71]. Jarraya et al [28] demonstrated that the LDA was better than the PCA for the selection of Gabor features.…”

Section: Hir-fb System: Horse Identity Recognition Based On Facial Bi...mentioning

confidence: 99%

Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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Horses, workers or riders need safety in a farm or a riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and recognize the identity of each worker or rider and horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse as well as the human gait biometrics due to their simplicity and intuitiveness in an uncontrolled environment. This work suggests a Siamese network based on DenseNet features for human gait recognition, a Sparse Neural Network (SNN) based on sparse features for horse face detection and a horse face recognition method based on Gabor features, LDA and SVM. Because of the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. The proposed systems achieved an average accuracy of gait recognition equal to 95% in the 0°view, 100% in the 90°view and 98.90% in the 180°view on Casia-B dataset, an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.INDEX TERMS Horse face detection, human gait recognition, neural network, siamese network, smart riding club, THDD.

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“…There is abundant visual content in these interesting regions. Many studies [18][19][20][21][22] are focus on the Gabor convolutional networks (GCNs) for facial expression recognition because Gabor filters can characterize the spatial frequency structure and hence can capture the features of the interesting regions of face efficiently. Skin color segmentation model is often used in preprocessing facial expression to detect the person face regions of the images captured from the real environments.…”

Section: Introductionmentioning

confidence: 99%

A fast facial expression recognition algorithm in the teaching and learning environments

Xie

2023

Optoelectronic Imaging and Multimedia Technology IX

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Learner’s Emotional state has an important impact on affective and cognitive processes for classroom teaching. It is quite necessary to detect learner’s emotion state unconsciously in teaching and learning processes. A fast facial expression recognition algorithm is presented to detect the emotional state of the learner in real learning environment. Gabor convolutional network (GCN) is used to classify the facial expression. The image extracted from teaching and learning environment need to be preprocessed for accelerating the expression recognition. A skin color segmentation model, generalized Gaussian mixture distribution (GGMD), is designed by using expectation and maximization (EM) algorithm to detect the facial area rapidly. Then a fast facial expression recognition algorithm is designed by using the skin color model and the GCN. Experiment results show the satisfied accuracy and excellent time performance of the system.

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A novel approach for facial expression recognition based on Gabor filters and genetic algorithm

Cited by 43 publications

References 59 publications

A high‐performance and lightweight framework for real‐time facial expression recognition

A high‐performance and lightweight framework for real‐time facial expression recognition

Biometric-Based Security System for Smart Riding Clubs

A fast facial expression recognition algorithm in the teaching and learning environments

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