Mr. Jianbang Qin Profile

Jianbang Qin

at Shanghai Jiao Tong Univ

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Proceedings Article | 4 January 2021 Paper

Event correlation for deception detection in long video

Liqian Gao, Jianbang Qin, Chunhui Du, Wei Guo

Proceedings Volume 11605, 116050E (2021) https://doi.org/10.1117/12.2586819

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Automated Deception Detection (ADD) is a challenging task and still under study as a visual analysis task. Based on the idea that human micro-expressions and body movements could be used as clues for ADD, many works have proposed some action recognition models for extracting face and body spatiotemporal features. However, these features are not sufficient evidence for deception; moreover, micro-expressions are difficult to detect and real-life deception samples are hard to collect, thus ADD still has many challenges. In this paper, we present a global two-stream network (GTSN), which not only extracts face and body features, but also utilizes the correlation between the deceptions. GTSN can improve the accuracy of deception detection by adding historical information based on the correlation between the deceptions. We build a dataset named Deception-Truthful (DT) for evaluating the performance of our proposed model. Experimental results demonstrate that our GTSN model outperforms other action recognition models used for ADD. Further, the proposed GTSN model also performs well on the real trial videos widely used in ADD.

Proceedings Article | 31 January 2020 Paper

Global evaluate-and-rescale network: an efficient model for action recognition

Jianbang Qin, Shuangjie Hu, Wei Guo

Proceedings Volume 11433, 114330T (2020) https://doi.org/10.1117/12.2559286

KEYWORDS: Convolution, RGB color model, Video, Network architectures, Video acceleration, Performance modeling, Optical flow, Data modeling, Neural networks

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Human action recognition in videos is a challenging task in the field of computer vision. Based on the idea of integrating temporal and spatial feature, many works have proposed a variety of methods for extracting spatiotemporal features, such as two-stream network and 3D convolution neural network (3D-CNN). However, due to the huge computational cost of optical flow for two-stream network and the huge number of parameters of 3D-CNN, the computational time required for action recognition is very long, therefore it is difficult to meet the requirements of real-time recognition. This paper aims to explore an efficient architecture of 3D-CNN for action recognition. On the premise of guaranteeing the recognition accuracy, we aim to greatly reduce the computational cost. In order to ensure good performance while reducing the amount of input data, we present Global Evaluate-and-Rescale (GER) Network, which is able to automatically extract the key frames of input data. We have evaluated the performance of our proposed model on two challenging human action recognition datasets UCF101 and HMDB51. The experimental results show that GER Network can reduce up to 50% of the computation time for recognition while achieving approximate accuracy with state-of-the-art 3D-CNN models.

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