A novel feature set for video emotion recognition

Mo, Shasha; Niu, Jianwei; Su, Yiming; Das, Sajal K.

doi:10.1016/j.neucom.2018.02.052

Cited by 32 publications

(15 citation statements)

References 28 publications

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“…For a designer, it is important to focus on attracting an audiences' attention, to entertain, and to persuade during the playing period. It is important to focus on the relationship between emotion and the feature of the video [13].…”

Section: Introductionmentioning

confidence: 99%

“…To give a list of musical features, we can see that audio contains tempo, mode, harmony, tonality, pitch, contour, interval, rhythm, sound level, timbre, timing, articulation, accents on specific notes, tone attacks, decays, and vibrato, where different emotions are correlated with different features. [13,41] The extraction and matching of the feature points in a video are important during the advertising production and emotion recognition [13]. The video can provide a kind of dynamic stimuli to viewers.…”

Section: Introductionmentioning

confidence: 99%

“…Videos, such as movies and MTV, contain visual and audio features that belong to five channels [46]: Visual image, print, and other graphics; music; speech; and sound or environmental effects. In the research of Mo [13], combination of Hilbert-Huang Transform(HHTC) features were extracted to cover the audio content, the visual content, and the dependencies between the audio and the visual signals, which can indicate the time-varying characteristics of visual and audio signals. These types of features can improve the recognition performance in Ekman's emotion model.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Emotion According to the Physical Elements of the Video

Zhang

Wen

Whang

2020

Sensors

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The increasing interest in the effects of emotion on cognitive, social, and neural processes creates a constant need for efficient and reliable techniques for emotion elicitation. Emotions are important in many areas, especially in advertising design and video production. The impact of emotions on the audience plays an important role. This paper analyzes the physical elements in a two-dimensional emotion map by extracting the physical elements of a video (color, light intensity, sound, etc.). We used k-nearest neighbors (K-NN), support vector machine (SVM), and multilayer perceptron (MLP) classifiers in the machine learning method to accurately predict the four dimensions that express emotions, as well as summarize the relationship between the two-dimensional emotion space and physical elements when designing and producing video.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition of Emotion According to the Physical Elements of the Video

Zhang

Wen

Whang

2020

Sensors

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“…A regressor is needed when mapping the extracted features to the continuous dimensional emotion space. Recently, one of the most popular regression method is support vector regression (SVR) [12,79,177]. For example, in [12], video features like audio, color, aesthetic are fed into SVR in the SVR-Standard experiment.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Affective Computing for Large-scale Heterogeneous Multimedia Data

Zhao

Wang

Soleymani

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., image, music, and video), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.

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“…But it could not differentiate anger and fear since they two exhibits more or less same emotions. The work proposed in [9] discusses about the emotion classification in video clips based on Hilbert-Huang Transform (HHT) based visual highlights, HHT-based sound highlights, and cross-relationship highlights. But this technique has a major limitation on predicting particular emotion since emotion clusters are used.…”

Section: Asmentioning

confidence: 99%

Automatic Caption Generation from Images Based on Facial Emotions

2020

IJRTE

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In the current era, the vast number of internet users accesses the social network sites like Facebook, Whatsapp, Twitter, and Instagram for uploading their images. User may upload all kind of images with different emotions. In this research article, we introduce novel facial emotion recognition technique using Convolutional Neural Network (CNN) approach and associated a caption automatically based on the identified emotion. We consider only still images for emotion classification. Using face detection algorithms faces were detected from the facial image which is followed by emotion prediction. We automated this detection process for images having frontal faces. In images having non-frontal faces, we manually plot the eye points for rotating the face in such a way that algorithm can detect faces easily. Inferences obtained as a result of experiments shows that the proposed work is capable of identifying minute differences between different emotions and predict accordingly. Based on the emotions identified corresponding captions are generated with better accuracy.

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A novel feature set for video emotion recognition

Cited by 32 publications

References 28 publications

Recognition of Emotion According to the Physical Elements of the Video

Recognition of Emotion According to the Physical Elements of the Video

Affective Computing for Large-scale Heterogeneous Multimedia Data

Automatic Caption Generation from Images Based on Facial Emotions

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