Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms

Pławiak, Paweł; Sośnicki, Tomasz; Niedźwiecki, Michał; Tabor, Zbisław; Rzecki, Krzysztof

doi:10.1109/tii.2016.2550528

Cited by 107 publications

(49 citation statements)

References 21 publications

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“…Emotion recognition process leverages techniques from multiple areas, such as psychology, signal processing and machine learning. Moreover, this process may utilize various input types, i.e., facial expressions [1][2][3][4][5], speech [6][7][8][9][10], gestures and body language [11][12][13][14][15] and physical signals such as electroencephalography (EEG) [16], electromyography (EMG) [17], electrodermal activity [18]. However, facial expressions have been studied most extensively.…”

Section: Introductionmentioning

confidence: 99%

Audiovisual emotion recognition in wild

Avots

Sapiński

Bachmann

et al. 2018

Machine Vision and Applications

133

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People express emotions through different modalities. Utilization of both verbal and nonverbal communication channels allows to create a system in which the emotional state is expressed more clearly and therefore easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. This article presents analysis of audiovisual information to recognize human emotions. A cross-corpus evaluation is done using three different databases as the training set (SAVEE, eNTERFACE'05 and RML) and AFEW (database simulating realworld conditions) as a testing set. Emotional speech is represented by commonly known audio and spectral features as well as MFCC coefficients. The SVM algorithm has been used for classification. In case of facial expression, faces in key frames are found using Viola-Jones face recognition algorithm and facial image emotion classification done by CNN (AlexNet). Multimodal emotion recognition is based on decision-level fusion. The performance of emotion recognition algorithm is compared with the validation of human decision makers.

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Section: Introductionmentioning

confidence: 99%

Audiovisual emotion recognition in wild

Avots

Sapiński

Bachmann

et al. 2018

Machine Vision and Applications

133

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“…These sets are shown in Figure 2. The mathematical notations of the membership calculations of the proposed triangular fuzzy-based transformation are given in Equations (1)- (4).…”

Section: Local Cross Pattern (Lcp)mentioning

confidence: 99%

“…Biometric identification systems are widely used in security-critical systems [1][2][3] and man-machine interfaces (MMI) [4]. They are used for personnel control and criminal monitoring/detection in areas such as military, hospital, airport, education [3,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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“…Emotions are evoked by different mechanisms such as events, objects, other people or phenomena that lead to various consequences manifesting in our body. Automatic affect recognition methods utilize various input types i.e., facial expressions [21,13,33,36,14], speech [25,18], gestures and body language [27,20] and physical signals such as electroencephalography (EEG) [16], electromyography (EMG) [17], electrodermal activity [11] etc. Although it has been investigated for many years, it is still an active research area because of growing interest in application exploiting avatars animation, neuromarketing and sociable robots [12].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition and Information Forensics

Zhang

Suter²,

YiShui³

et al. 2019

Lecture Notes in Computer Science

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People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman's emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.

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Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms

Cited by 107 publications

References 21 publications

Audiovisual emotion recognition in wild

Audiovisual emotion recognition in wild

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Pattern Recognition and Information Forensics

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