Facial expression recognition using enhanced local binary patterns

Ekweariri, Augustine Nnamdi; Yurtkan, Kamil

doi:10.1109/cicn.2017.8319353

Cited by 23 publications

(8 citation statements)

References 10 publications

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“…The binary image was formed from the high and low variance code matrix union and became the reference feature selection for LBPs. The experiment conducted on BU-3DFE showed better performance as reported in [4].…”

Section: A: Gabor Waveletmentioning

confidence: 57%

“…From the psychological point of view, the categories of human emotional states are into six basic emotions; sad, happy, fear, surprise, anger and disgust [1]. According to the study conducted by [2], facial expression carried a larger percentage of communication information in man than any other non-verbal medium like hand gesture, body gesture, and text [3] [4]. A man without difficulty can easily interpret expression display in the face, but the automation of this task in the machine remains a challenge [4].…”

Section: Introductionmentioning

confidence: 99%

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Facial Expression Recognition: A Review of Trends and Techniques

Ekundayo

Viriri

2021

IEEE Access

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Facial Expression Recognition (FER) is presently the aspect of cognitive and affective computing with the most attention and popularity, aided by its vast application areas. Several studies have been conducted on FER, and many review works are also available. The existing FER review works only give an account of FER models capable of predicting the basic expressions. None of the works considers intensity estimation of an emotion; neither do they include studies that address data annotation inconsistencies and correlation among labels in their works. This work first introduces some identified FER application areas and provides a discussion on recognised FER challenges. We proceed to provide a comprehensive FER review in three different machine learning problem definitions: Single Label Learning (SLL)-which presents FER as a multiclass problem, Multilabel Learning (MLL)-that resolves the ambiguity nature of FER, and Label Distribution Learning-that recovers the distribution of emotion in FER data annotation. We also include studies on expression intensity estimation from the face. Furthermore, popularly employed FER models are thoroughly and carefully discussed in handcrafted, conventional machine learning and deep learning models. We finally itemise some recognise unresolved issues and also suggest future research areas in the field.

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Section: A: Gabor Waveletmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition: A Review of Trends and Techniques

Ekundayo

Viriri

2021

IEEE Access

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“…The performance of conventional approaches usually decreases when applied to datasets with large variability. The robustness of the handcrafted feature extraction method and the conformity of each stage bind the conventional approach performance [5], [19].…”

Section: Introductionmentioning

confidence: 94%

“…There are two categories of classifiers, namely conventional and deep learning. Some classifiers that belong to the first category, such as k-nearest neighbors (KNN) [19], support vector machine (SVM) [20], and Adaboost (adaptive boosting) [21], have been applied in FER. The conventional approach results in high accuracy on datasets with less variability, such as a collection of frontal face images with the same illumination.…”

Section: Introductionmentioning

confidence: 99%

“…Developing a FER system on datasets with a small number of images containing a variety of illuminations and head poses is a challenge. [19] presents feature selection based on the variance of LBP images and classification based on distance measurement, which obtains an accuracy of 66.67%. Research by Islam et al [22] extracts features in the segmented parts of the expression area (right and left eye, nose, mouth) using Gabor filters before being applied in the classification stage using the extreme learning machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

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Selective local binary pattern with convolutional neural network for facial expression recognition

Zulkarnain

Suciati

2022

IJECE

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<span lang="EN-US">Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.</span>

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