Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

• Main Authors: Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, Kang Ryoung Park
• Format: Article
• Language: English
• Published: MDPI AG 2017-03-01
• Series: Sensors
• Subjects: person recognition; surveillance systems; visible light and thermal cameras; histogram of oriented gradients; convolutional neural network
• Online Access: http://www.mdpi.com/1424-8220/17/3/605

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.