Facial Expression Recognition Based on Edge Detection

Chen, Xiaoming; Cheng, Wushan

doi:10.5121/ijcses.2015.6201

Cited by 22 publications

(20 citation statements)

References 3 publications

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“…Regarding the JAFFE database, the leave-one-out test result obtained surpassed the LDA + SVM [4] model by 1%, whereas the model that uses the same extraction and classification techniques with HOG and SVM [5] produced results that were less than those of the proposed model by 2.41%. The results of the K-fold tests also surpassed those of the previous models, especially the 10-fold model that had a huge accuracy difference of 5.70%.…”

Section: Comparisonmentioning

confidence: 98%

“…Some researchers decided to segment the face rather than take it as a whole and changed their extraction method. Chen et al [5] applied facial segmentation to separate the eyes and mouth from the face before applying the histogram-oriented gradients (HOG) feature and classifying the image using an SVM. Also, Chen et al [5] used a patch-based Gabor filter after segmenting each component of the face.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [5] applied facial segmentation to separate the eyes and mouth from the face before applying the histogram-oriented gradients (HOG) feature and classifying the image using an SVM. Also, Chen et al [5] used a patch-based Gabor filter after segmenting each component of the face. Others decided to combine several extraction methods and then speed up the computation time.…”

Section: Introductionmentioning

confidence: 99%

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Facial expression recognition using three-stage support vector machines

Dagher

Dahdah

Shakik

2019

Vis. Comput. Ind. Biomed. Art

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Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.

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Section: Comparisonmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facial expression recognition using three-stage support vector machines

Dagher

Dahdah

Shakik

2019

Vis. Comput. Ind. Biomed. Art

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“…Mainly two steps, namely, feature extraction and classification, are associated with the FER task. Conventional features, such as Gobar wavelets [4], curves [12], scale-invariant feature transform [21], HOG [8], LBP [6], minutiae points [11], Haar wavelet [5], HBIV [9], DBN [10], and edges [38], were exploited with advanced domain comprehension in the first step. In the second step, support vector machine (SVM) [39], feedforward neural network [40], and extreme learning machine [41] were adopted for classification.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Karnati

Seal

Krejcar

et al. 2021

IEEE Trans. Instrum. Meas.

130

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An image is worth a thousand words; hence, a face image illustrates extensive details about the specification, gender, age, and emotional states of mind. Facial expressions play an important role in community-based interactions and are often used in the behavioral analysis of emotions. Recognition of automatic facial expressions from a facial image is a challenging task in the computer vision community and admits a large set of applications, such as driver safety, human-computer interactions, health care, behavioral science, video conferencing, cognitive science, and others. In this work, a deep-learning-based scheme is proposed for identifying the facial expression of a person. The proposed method consists of two parts. The former one finds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model. The proposed DCNN has two branches. The first branch explores geometric features, such as edges, curves, and lines, whereas holistic features are extracted by the second branch. Finally, the scorelevel fusion technique is adopted to compute the final classification score. The proposed method along with 25 state-of-the-art methods is implemented on five benchmark available databases,

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“…The researcher described edge detection algorithms for eyes and lips variation during human communication. canny edge detection (CED) provides better results on following facial expressions such as Sad 91.33%, Smile 99.44%, and Surprise 95.71% [11].…”

Section: Introductionmentioning

confidence: 99%

Emotion Based Facial Expression Detection Using Machine Learning Approach

Jamal¹

2020

Journal of Applied and Emerging Sciences

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In this study emotion-based face expression recognition framework has been proposed using a machine vision (MV) approach. The face emotion dataset has been collected local survey in Bahawalpur city dataset divide into 3 classes happy, sad, and angry. A total of 600 images of size (256 x 256) were transformed into a gray level format and employed a median filter for noise removal. Three nonoverlapping regions of interest (ROIs) of size (50 x 50) have been taken and analyze 1800 (600 x 3) ROIs on the overall dataset. Total 45 Statistical features named as texture, histogram, and binary features were extracted. Select optimize features using the correlationbased feature section technique. The optimized dataset employed of MV classifiers namely random forest (RF), logistic (Lg), and J48 are obtained very promising accuracy 96.33%, 95.67%, and 95.33% respectively.

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Facial Expression Recognition Based on Edge Detection

Abstract: ABSTRACT

Cited by 22 publications

References 3 publications

Facial expression recognition using three-stage support vector machines

Facial expression recognition using three-stage support vector machines

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Emotion Based Facial Expression Detection Using Machine Learning Approach

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