Deep supervised hashing for gait retrieval


Sayeed, Md. Shohel and Min, Pa Pa and Ong, Thian Song (2022) Deep supervised hashing for gait retrieval. F1000Research, 10. p. 1038. ISSN 2046-1402

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Gait recognition is perceived as the most promising biometric approach for future decades especially because of its efficient applicability in surveillance systems. Due to recent growth in the use of gait biometrics across surveillance systems, the ability to rapidly search for the required data has become an emerging need. Therefore, we addressed the gait retrieval problem, which retrieves people with gaits similar to a query subject from a large-scale dataset. Methods: This paper presents the deep gait retrieval hashing (DGRH) model to address the gait retrieval problem for large-scale datasets. Our proposed method is based on a supervised hashing method with a deep convolutional network. We use the ability of the convolutional neural network (CNN) to capture the semantic gait features for feature representation and learn the compact hash codes with the compatible hash function. Therefore, our DGRH model combines gait feature learning with binary hash codes. In addition, the learning loss is designed with a classification loss function that learns to preserve similarity and a quantization loss function that controls the quality of the hash codes.

Item Type: Article
Uncontrolled Keywords: Gait Retrieval, Deep Supervised Hashing, Convolutional Neural Network, Binary codes
Subjects: Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 Aug 2022 01:12
Last Modified: 01 Aug 2022 01:12


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