Gender Recognition Using 3-D Human Body Shapes

Tang, Jinshan; Liu, Xiaoming; Cheng, Huaining; Robinette, Kathleen M.

doi:10.1109/tsmcc.2011.2104950

Cited by 30 publications

(24 citation statements)

References 45 publications

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“…Different classification methods were tested in Tang's paper [16] . These methods include K-Nearest Neighbor (KNN) algorithm, artificial neural network (ANN), and Support Vector Machine (SVM).…”

Section: Resultsmentioning

confidence: 99%

Gender recognition from torso features using elliptic fourier descriptors

Wang

Xia

2014

2014 7th International Conference on Biomedical Engineering and Informatics

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Gender recognition has important applications in apparel design, social security, and human-computer interaction systems. In this paper, we investigate gender-recognition technologies using 3-D human body shape. The front and side silhouettes from 459 female subjects and 107 male subjects were extracted and then modeled using normalized Elliptic Fourier descriptors. Principal Component Analysis (PCA) was conducted to summarize the information contained by the EF coefficients. A back propagation (BP) neural network with 33 inputs, 2 outputs and 10 hidden layers was adopted to gender recognition. The research demonstrates that the gender recognition from torso features has achieved a considerably high recognition rate. Moreover, the combination of the PCA and BP neural network have provided effective ways for gender recognition and overcome some limitations in other technologies.

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Section: Resultsmentioning

confidence: 99%

Gender recognition from torso features using elliptic fourier descriptors

Wang

Xia

2014

2014 7th International Conference on Biomedical Engineering and Informatics

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“…Requires an expensive capturing device (scanner) to obtain 3D information of the human body [15,16]. …”

Section: Figurementioning

confidence: 99%

“…This method is efficient for reducing the effects of background regions on the extracted HOG features and consequently helps to enhance the recognition accuracy. In addition to the use of a single image for gender recognition, several other studies have used a sequence of images [13,14] or the three-dimensional (3D) shape of the human body [15,16] for gender recognition. Although the methods mentioned previously have demonstrated recognition of gender information from images of the human body, their recognition accuracy is limited by the use of predesigned and/or unsuitable feature extraction methods, such as HOG, BIFs, or weighted HOG.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Kim

Hong

et al. 2017

Sensors

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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.

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“…Looking at methods that take 3D information into account, [12] recognize gender from a large set (2484 persons) of 360 • full-body high-resolution laser scans created with an expensive stationary scanner. The approach requires several costly steps including hole-filling, mesh smoothing and normal computation and assumes no clothing.…”

Section: Related Workmentioning

confidence: 99%

Real-time full-body human gender recognition in (RGB)-D data

Linder

Wehner

Arras

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Understanding social context is an important skill for robots that share a space with humans. In this paper, we address the problem of recognizing gender, a key piece of information when interacting with people and understanding human social relations and rules. Unlike previous work which typically considered faces or frontal body views in image data, we address the problem of recognizing gender in RGB-D data from side and back views as well. We present a large, genderbalanced, annotated, multi-perspective RGB-D dataset with full-body views of over a hundred different persons captured with both the Kinect v1 and Kinect v2 sensor. We then learn and compare several classifiers on the Kinect v2 data using a HOG baseline, two state-of-the-art deep-learning methods, and a recent tessellation-based learning approach. Originally developed for person detection in 3D data, the latter is able to learn the best selection, location and scale of a set of simple point cloud features. We show that for gender recognition, it outperforms the other approaches for both standing and walking people while being very efficient to compute with classification rates up to 150 Hz.

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Gender Recognition Using 3-D Human Body Shapes

Cited by 30 publications

References 45 publications

Gender recognition from torso features using elliptic fourier descriptors

Gender recognition from torso features using elliptic fourier descriptors

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

Real-time full-body human gender recognition in (RGB)-D data

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