Using Facial Expression Recognition for Crowd Monitoring

Holder, Ross Philip; Tapamo, Jules-Raymond

doi:10.1007/978-3-319-75786-5_37

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Constructing a framework for monitoring emotions in a group is highly relevant, as emotions lie at the basis of behaviour of a group [4], [5]. Monitoring emotion, preferably in real-time, can be employed to predict behaviour and intervene when necessary [6].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

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Ridge Structure Enhancement for Optical Coherence Tomography Latent Fingerprint Images

Mgaga,

Khanyile,

Thwala

et al. 2023

2023 17th International Conference on Signal-Image Technology &Amp;amp; Internet-Based Systems (SITIS)

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The Innovative Role of Process Mining in building Face Re-identification Trajectory

Alharbi

2024

Eng. Technol. Appl. Sci. Res.

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Face recognition and tracking technology have witnessed significant advancements during the recent decades. These advancements include improved accuracy and speed in identifying and tracking individuals, as well as the ability to recognize faces in various scale and lighting conditions. Leveraging the potential of face recognition and tracking, this article explores the integration of process mining techniques to discover and visualize face’s appearing trajectories in crowd scenarios, aiming to enhance crowd security, surveillance, and personal identification. Notably, existing face recognition tools typically focus on bounding box localization, neglecting the utilization of face coordinates to construct trajectory models upon face re-identification. In this paper, full system architecture for building a trajectory model of re-identified faces in a crowd is proposed. This approach significantly helped in building a large database of visitor faces, and the proposed trajectory model resulted in a high rate of true positive face re-identification.

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Using Facial Expression Recognition for Crowd Monitoring

Cited by 3 publications

References 27 publications

Automatic Emotion Recognition for Groups: A Review

Automatic Emotion Recognition for Groups: A Review

Ridge Structure Enhancement for Optical Coherence Tomography Latent Fingerprint Images

The Innovative Role of Process Mining in building Face Re-identification Trajectory

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