Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary pat...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-03, Vol.17 (3), p.637
Main Authors: Nguyen, Dat Tien, Kim, Ki Wan, Hong, Hyung Gil, Koo, Ja Hyung, Kim, Min Cheol, Park, Kang Ryoung
Format: Article
Language:English
Subjects:
Computer vision
convolutional neural network
Feature extraction
Gender
gender recognition
Histograms
human body images
Image classification
Neural networks
Surveillance systems
visible-light and thermal camera videos
Vision systems
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Summary:Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17030637