Emotion Recognition of a Group of People in Video Analytics Using Deep Off-the-Shelf Image Embeddings

Tarasov, A.V.; Savchenko, Andrey V.

doi:10.1007/978-3-030-11027-7_19

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…For example, experiments from paper [ 17 ] clearly demonstrate that usage of TinyFaces detector provide lower accuracy of group emotion recognition when compared to Viola-Jones detector, if VGGFace features are used [ 15 ]. In such case, it is possible that we should use other facial descriptors trained on facial images with small resolution [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

“…Prediction of group-level emotion [ 1 ] and cohesiveness is very useful for various companies in order to analyze employee’s emotional state throughout the day and build a relationship between their emotional state and group cohesion [ 2 ]. As the cohesiveness of a group is a crucial indicator of success of a group of people, the problem of predicting the perceived cohesiveness of a group of people in image becomes one of the main tasks in the EmotiW (Emotion Recognition in the Wild) 2019 challenge [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

Gavrikov¹,

Savchenko²

2021

Communications in Computer and Information Science

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In this paper we study the problem of predicting the cohesiveness and emotion of a group of people in photo. We proposed a fast approach, consisting of face detection by using MTCNN, aggregation of facial features (age, gender and embeddings) extracted by multi-task MobileNet, prediction of group cohesion and classification of emotional background using multi-output convolution neural network. Experimental study on the Group Affect Dataset from EmotiW 2019 challenge demonstrated that our approach allows to achieve an improvement of quality and even to reduce the running time of an algorithm’s work when compared to known solutions. As a result, we obtained mean squared error 0.63 for cohesion prediction, which is 0.21 lower when compared to baseline CapsNet.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

Gavrikov¹,

Savchenko²

2021

Communications in Computer and Information Science

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“…are either a combination of non-neural classifiers [63] or a combination of a Long Short-Term Memory (LSTM) and a dense layer [64]. In [65], CNNs are employed for emotion prediction for individual faces, fusing the individual predictions to form a group prediction.…”

Section: Images and Videosmentioning

confidence: 99%

“…In these studies, features are in some way combined before being fed to a classifier. Liu et al [75] use a simple average of face features, and [63] also average individual embeddings to get one image feature. In [21] and [22], group arousal is calculated from the ratio of responding individuals to all individuals.…”

Section: Feature-level Fusionmentioning

confidence: 99%

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Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

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This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

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Scene Recognition in User Preference Prediction Based on Classification of Deep Embeddings and Object Detection

Savchenko

Rassadin

2019

Advances in Neural Networks – ISNN 2019

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Emotion Recognition of a Group of People in Video Analytics Using Deep Off-the-Shelf Image Embeddings

Cited by 6 publications

References 12 publications

Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

Automatic Emotion Recognition for Groups: A Review

Scene Recognition in User Preference Prediction Based on Classification of Deep Embeddings and Object Detection

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