CNN-Based Multimodal Human Recognition in Surveillance Environments
Open accessAbstract
In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research being performed on indoor human recognition. Previous research on indoor recognition has mainly focused on face recognition because the camera is usually closer to a person in an indoor environment than an outdoor environment. However, due to the nature of indoor surveillance cameras, which are installed near the ceiling and capture images from above in a downward direction, people do not look directly at the cameras in most cases. Thus, it is often difficult to capture front face images, and when this is the case, facial recognition accuracy is greatly reduced. To overcome this problem, we can consider using the face and body for human recognition. However, when images are captured by indoor cameras rather than outdoor cameras, in many cases only part of the target body is included in the camera viewing angle and only part of the body is captured, which reduces the accuracy of human recognition. To address all of these problems, this paper proposes a multimodal human recognition method that uses both the face and body and is based on a deep convolutional neural network (CNN). Specifically, to solve the problem of not capturing part of the body, the results of recognizing the face and body through separate CNNs of VGG Face-16 and ResNet-50 are combined based on the score-level fusion by Weighted Sum rule to improve recognition performance. The results of experiments conducted using the custom-made Dongguk face and body database (DFB-DB1) and the open ChokePoint database demonstrate that the method proposed in this study achieves high recognition accuracy (the equal error rates of 1.52% and 0.58%, respectively) in comparison to face or body single modality-based recognition and other methods used in previous studies.
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Koo et al. developed a multimodal human recognition framework based on face and body information extracted from deep CNNs, employing late fusion to merge high-level features across modalities.
“In [73], Koo et al. developed a multimodal human recognition framework based on face and body information extracted from deep CNNs. They employed the late fusion policy to merge the high-level features across the different modalities.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [73]
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Xu, H. F., Kong, J., Qi, M., & Lu, Y. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-8086-4_11 · Cited by 1 · see citing works
Face Verification for Person Re-Identification from Surveillance Camera and Drone-based Videos
Mathew, V., Ramesh, K., Toby, T., & Chacko, A. M. (2021) · Journal of Computer Science · doi:10.3844/jcssp.2021.639.656 · Cited by 1 · see citing works
Human Identification and Recognition in Depth Imaging EnvironmentsPreprint
Czarnuch, S. M., & Zizui, C. H. E. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5353425
- Authors
- Koo, J. H., Cho, S. W., Baek, N. R., Kim, M. C., & Park, K. R.
- Affiliations
- Dongguk University (KR)
- Corresponding author
- Park, Kang Ryoung
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-09-11
- Volume
- 18
- Issue
- 9
- Pages
- 3040
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6164664
- OpenAlex
- W2890686698
- Cited by
- 30
- References
- 73
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 5 · 2022: 5 · 2021: 9 · 2020: 7
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2015R1D1A1A01056761, NRF- 2016M3A9E1915855)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- indoor surveillance, face recognition, body recognition, score-level fusion, weighted sum fusion, ResNet50
- MeSH terms
- Body Constitution, Face, Female, Humans, Male, Databases, Factual, Neural Networks, Computer, Biometric Identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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