A facial expression emotion recognition based human-robot interaction system

Liu, Zhentao; Wu, Min; Cao, Weihua; Chen, Luefeng; Xu, Jianping; Zhang, Ri; Zhou, Mengtian; Mao, Jun-Wei

doi:10.1109/jas.2017.7510622

Cited by 133 publications

(46 citation statements)

References 18 publications

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“…Emotions play an important role in many speech-based human-computer interface applications [1][2][3]. Therefore, in recent years, Speech Emotion Recognition (SER) has attracted increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Using Scalogram Based Deep Structure

Aghajani

Afrakoti

2020

IJE

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Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concatenated Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The CNN can be used to learn local salient features from speech signals, images, and videos. Moreover, the RNNs have been used in many sequential data processing tasks in order to learn long-term dependencies between the local features. A combination of these two gives us the advantage of the strengths of both networks. In the proposed method, CNN has been applied directly to a scalogram of speech signals. Then, the attention-mechanism-based RNN model was used to learn long-term temporal relationships of the learned features. Experiments on various data such as RAVDESS, SAVEE, and Emo-DB demonstrate the effectiveness of the proposed SER method.

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Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Using Scalogram Based Deep Structure

Aghajani

Afrakoti

2020

IJE

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“…During the process of eye gaze estimation and tracking, we need to determine the precise pixel location of important key points of the eye center for a single given RGB image. Moreover, achieving accurate eye center localization is useful for higher level tasks [2][3][4][5][6][7] such as human attention control, driver monitoring system and sentiment analysis, and also serves as a fundamental tool in fields such as human computer interaction and animation.…”

Section: Introductionmentioning

confidence: 99%

Accurate and robust eye center localization via fully convolutional networks

Xia

Wang

2019

IEEE/CAA J. Autom. Sinica

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Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an endto-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

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“…The literature describes various classification algorithms to be used after the feature extraction phase in various applications. These include Artificial Neural Networks (ANN) [4], AdaBoost classification [7], Support Vector Machine (SVM) [6], [8][9], k-Nearest neighbor (kNN) [3], random forests (RF) [6], Extreme Learning Machine (ELM) [5].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Facial Expressions using GFE, LBP And Hog Feature Extraction Techniques

Bharate*¹,

Chaudhari²,

Chaudhari³

2019

IJITEE

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The social interaction of human beings is many times influenced by non-verbal communication, especially facial expressions. In societal life face of a human being is mostly observed by surrounding people to know the inner feelings. Thus, face forms a significant source of expressing human emotions, typically categorized into surprise, anger, fear, disgust, sad and happy. In the variety of behavioral science fields, emotion recognition has a significant role to play. The present paper describes a system in which preprocessing is performed by median filtering. Before extracting features, the watershed segmentation is applied to get the required characteristics of an image. In this paper, the Gamma based Feature Extraction (GFE), Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) technique have been used for feature extraction. The LBP algorithm is additionally tested with and without application of gamma correction using GFE. Two classifiers, namely kNN and SVM, have been employed, and their performance is compared. kNN and SVM, being supervised classifiers, can aid in better accuracy with proper training

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A facial expression emotion recognition based human-robot interaction system

Cited by 133 publications

References 18 publications

Speech Emotion Recognition Using Scalogram Based Deep Structure

Speech Emotion Recognition Using Scalogram Based Deep Structure

Accurate and robust eye center localization via fully convolutional networks

Emotion Recognition from Facial Expressions using GFE, LBP And Hog Feature Extraction Techniques

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