Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network
Human identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8502031/ |
| _version_ | 1818914681038831616 |
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| author | Ganbayar Batchuluun Hyo Sik Yoon Jin Kyu Kang Kang Ryoung Park |
| author_facet | Ganbayar Batchuluun Hyo Sik Yoon Jin Kyu Kang Kang Ryoung Park |
| author_sort | Ganbayar Batchuluun |
| collection | DOAJ |
| description | Human identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of deep learning algorithms, studies that are based on the human gait using convolutional neural networks (CNNs) and long short-term memory (LSTM) have achieved promising performance for human identification. However, CNN and LSTM-based methods have the limitation of having higher loss of temporal and spatial information, respectively. In our approach, we use shallow CNN stacked with LSTM and deep CNN followed by score fusion to capture more spatial and temporal features. In addition, there have been a few studies regarding gait-based human identification based on the front and back view images of humans captured in low-illumination environments. This makes it difficult to extract conventional features, such as skeleton joints, cycle, cadence, and the lengths of walking strides. To overcome these problems, we designed our method considering the front and back view images captured in both highand lowillumination environments. The experimental results obtained using a self-collected database and the open database of the institute of automation Chinese academy of sciences gait dataset C show that the proposed method outperforms previous methods. |
| first_indexed | 2024-12-19T23:50:15Z |
| format | Article |
| id | doaj.art-9960f2f2c2ef40408c46e97f4601b962 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:50:15Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9960f2f2c2ef40408c46e97f4601b9622022-12-21T20:01:09ZengIEEEIEEE Access2169-35362018-01-016631646318610.1109/ACCESS.2018.28768908502031Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural NetworkGanbayar Batchuluun0Hyo Sik Yoon1Jin Kyu Kang2Kang Ryoung Park3https://orcid.org/0000-0002-1214-9510Division of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaHuman identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of deep learning algorithms, studies that are based on the human gait using convolutional neural networks (CNNs) and long short-term memory (LSTM) have achieved promising performance for human identification. However, CNN and LSTM-based methods have the limitation of having higher loss of temporal and spatial information, respectively. In our approach, we use shallow CNN stacked with LSTM and deep CNN followed by score fusion to capture more spatial and temporal features. In addition, there have been a few studies regarding gait-based human identification based on the front and back view images of humans captured in low-illumination environments. This makes it difficult to extract conventional features, such as skeleton joints, cycle, cadence, and the lengths of walking strides. To overcome these problems, we designed our method considering the front and back view images captured in both highand lowillumination environments. The experimental results obtained using a self-collected database and the open database of the institute of automation Chinese academy of sciences gait dataset C show that the proposed method outperforms previous methods.https://ieeexplore.ieee.org/document/8502031/Human identificationshallow CNN stacked LSTMdeep CNNthermal image |
| spellingShingle | Ganbayar Batchuluun Hyo Sik Yoon Jin Kyu Kang Kang Ryoung Park Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network IEEE Access Human identification shallow CNN stacked LSTM deep CNN thermal image |
| title | Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network |
| title_full | Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network |
| title_fullStr | Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network |
| title_full_unstemmed | Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network |
| title_short | Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network |
| title_sort | gait based human identification by combining shallow convolutional neural network stacked long short term memory and deep convolutional neural network |
| topic | Human identification shallow CNN stacked LSTM deep CNN thermal image |
| url | https://ieeexplore.ieee.org/document/8502031/ |
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