Facial Expressions Recognition: Development and Application to HMI

Zekhnine, Chérifa; Eddine, Berrached Nasr

doi:10.1007/978-3-319-89743-1_49

Cited by 4 publications

(2 citation statements)

References 13 publications

(11 reference statements)

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“…The purpose was to make recognition system sensitive with facial muscle deformation. Application OF facial emotion recognition has extended to command a mobile robot in [12]. Based on deep convolution neural network comprise of convolution layer and deep residual blocks six different emotions have been classified in [13].…”

Section: Related Workmentioning

confidence: 99%

Human Facial Emotion Recognition using Adaptive Sigmoidal Transfer Function in MLP Neural Network

Unnisa,

2019

IJEAT

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The human face is very sensitive towards inner feelings particularly with different state of mind under various conditions. The facial expression has used in computer vision to understand the human response against stimuli. But the facial expression is also having the nature of variability and controllability hence its complete generalization from a computer vision point of view is very difficult and challenging, though acceptable performances can be achieved. In this paper, a twostage based facial expression recognition model which carry the Principal component analysis as a feature extractor in the first stage and self-adaptive based activation function in feedforward neural network as a classifier in the second stage have applied. Use of principal component analysis reduces the dimension of features while the adaptive slope of transfer function provides another parameter along with weights to change in making learning faster and accurate. Six most dominant state of facial emotion like angry, surprise, sadness, normal, happy and fear have considered in this paper and performances have been tested over variable expressions. The benefit of the proposed model of self-adaptive activation function has verified through the benchmark XOR problem classification.

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Section: Related Workmentioning

confidence: 99%

Human Facial Emotion Recognition using Adaptive Sigmoidal Transfer Function in MLP Neural Network

Unnisa,

2019

IJEAT

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“…The relative positions of the neighbouring pixels can be obtained from GLCM. In the research article "facial expression recognition development and applications to HMI" (Cherifa and Eddine, 2018) shows how HMI can be effectively benefited by facial expression recognition systems.…”

Section: Related Workmentioning

confidence: 99%

Symbolic Aggregate approXimation-Local Binary Pattern Feature Descriptor Combination for Automatic Facial Expression Recognition

Sumithra¹,

Rahiman²

2019

Journal of Computer Science

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Automatic identification of facial expression is a significant research area which is anticipated for real time processing in Human-Computer Interaction domain. Along with an efficient classifier for assigning the class label to each of the input face image, it is very necessary to have a strong feature vector for training the classifier. This paper proposes an effectual combination of Local Binary Pattern and Symbolic Aggregate approXimation method for the feature vector generation for the classifier. Twenty one facial patches are extracted from the face image and the LBP value and SAX string for these twenty one patches are utilised for feature vector generation. The feature vectors of images are submitted to the Ensemble Bag classifier for training purpose. Images which were not used for training is used for testing. An average accuracy of 98.7% was obtained when tested on JAFFE data set for seven expressions and an accuracy of 96.96% was obtained for nine expressions on fused database. A detailed analysis of the testing conducted on images with partial occlusion and illumination variance are presented here.

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