Effective semantic features for facial expressions recognition using SVM

Hsieh, Chen-Chiung; Hsih, Mei-Hua; Jiang, Meng-Kai; Cheng, Yun-Maw; Liang, En-Hui

doi:10.1007/s11042-015-2598-1

Cited by 43 publications

(12 citation statements)

References 45 publications

(87 reference statements)

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“…By comparing these results with the results of some recent papers on the real-time system, note that in [45] used SVM to classify 6 facial expressions and the recognition rate was about 93% in a real-world environment. Also, in [46] study evaluate the performance of three different classifiers using Multilayer perceptrons (MLPS), SVM and AdaBoost algorithms, the accuracy rate of facial expression recognition system achieved more than 90% for expressions appearing in an image sequence.…”

mentioning

confidence: 79%

A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

ELLaban¹,

Ewees²,

AbdElrazek³

2017

IJCA

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Faces are a unique feature of human being that can detect a great deal of information about age, health, personalities and feelings. Facial Expressions are the main sources in determining the internal impressions of the individual. RealTime system for facial expression recognition is able to detect and locate human faces in image sequences obtained in real environments then extracts expression features from these images finally recognize facial expressions. In this paper, the proposed system presents a real-time system for facial expression recognition that aims to recognize 8 basic facial expressions of students: anger, disgust, fear, happy, nervous, sad, surprise and natural inside E-learning environment. The primary objective is to use k-NN and SVM classifiers to test the efficiency of the proposed system and compared the results of them. There are some techniques has been used in this study for facial expression recognition such as Viola-Jones approaches to detect a face from images, Gabor Feature approach to extract features, and Principal Component Analysis (PCA) to select features and k-NN, SVM classifiers to recognize expressions from facial image.. The result showed that the SVM classifier has the best recognition rate in general thank-NN classifier. From these results, it can say that SVM classifier is more suitable for recognition of facial expression in a real-time system.

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mentioning

confidence: 79%

A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

ELLaban¹,

Ewees²,

AbdElrazek³

2017

IJCA

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“…ey used convolution neural network (CNN) to extract features from optimized active face regions. e method used by Hsieh et al [8] was based on the active shape model (ASM). ey employed ASM to extract different facial expression regions.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

Mahmood

Hussain

Iqbal

et al. 2019

Mathematical Problems in Engineering

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Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

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“…The fuzzy cluster method is applied and approved. The equation of fuzzy cluster is as shown in equation 7[ [16][17][18][19][20][21].…”

Section: Classification Stagementioning

confidence: 99%

Glaucoma Diagonsis Fcm_tk Algorithm Based on Funds Camera

Aouf

2018

IJICIS

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Glaucoma is considered one of the most important human eye diseases that causes loss of vision. The main objective of this paper is extracting the features from fundus image to evaluate and minimize the risk of Glaucoma. This evaluation based on the Cup-to-Disc Ratio (CDR) of a color retinal fundus image. Actually, CDR is the actual value for the evolution of the risk of Glaucoma. Features used to evaluate the Glaucoma risk are extracted by detection and segmentation process. In this paper a method is proposed for Glaucoma image classification. The proposed method is as follows: the features extracted by discrete wavelet transform (DWT) and then followed by Teager-Kiaser Energy (TKE) operator to improve the detection quality, then classification is done based on fuzzy clustering method. The results showed that the proposed methods succeeded to diagnose the Glaucoma disease with high accuracy compared with the other state of the art techniques.

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Effective semantic features for facial expressions recognition using SVM

Cited by 43 publications

References 45 publications

A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

Glaucoma Diagonsis Fcm_tk Algorithm Based on Funds Camera

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