Abstract
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.
Highlights
Previous biometrics studies have used various modalities, including the face, fingerprints, body, irises, retinas veins, and voice [1,2,3,4,5,6,7,8,9]
This study focuses on cases that often occur in indoor surveillance camera environments in which a person is approaching or moving further away from the camera; the proposed method is the first approach for the multimodal human recognition that separately recognizes face and body regions in a single image and combines them
This paper proposed a multimodal human recognition method that uses both the face and body regions in indoor surveillance camera environments, and is based on deep convolutional neural network (CNN)
Summary
Previous biometrics studies have used various modalities, including the face, fingerprints, body, irises, retinas veins, and voice [1,2,3,4,5,6,7,8,9]. In a typical surveillance camera environment, it is difficult to use fingerprints or vein recognition, so face, body, and iris methods have been considered. In a surveillance environment, the camera is normally installed above the user and captures images in a downward direction, so it mainly takes off-angle images that capture the user’s iris at an angle. In such circumstances, the recognition accuracy is greatly reduced [9].
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