Nguyen Dat Tien, Kim Ki Wan, Hong Hyung Gil, Koo Ja Hyung, Kim Min Cheol, Park Kang Ryoung
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea.
Sensors (Basel). 2017 Mar 20;17(3):637. doi: 10.3390/s17030637.
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.
提取强大的图像特征在计算机视觉系统中起着重要作用。此前已经提出了许多方法来为各种计算机视觉应用提取图像特征，例如尺度不变特征变换（SIFT）、加速鲁棒特征（SURF）、局部二值模式（LBP）、方向梯度直方图（HOG）和加权HOG。最近，用于计算机视觉中图像特征提取和分类的卷积神经网络（CNN）方法已被应用于各种应用中。在本研究中，我们提出了一种新的性别识别方法，该方法基于通过CNN从可见光和热成像相机视频中提取特征，在监控系统的观察场景中识别男性和女性。实验结果证实了我们提出的方法在使用人体图像解决性别识别问题上优于现有最先进的识别方法。
