A convolutional neural network for pedestrian gender recognition

Authors:
Choon-Boon Ng;Yong-Haur Tay;Bok-Min Goi
Affiliations:
Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia;Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia;Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 7
Cited 0

Pedestrian Detection Using Wavelet Templates

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Synergistic Face Detection and Pose Estimation with Energy-Based Models

The Journal of Machine Learning Research
Gender recognition from body

MM '08 Proceedings of the 16th ACM international conference on Multimedia
Gender from body: a biologically-inspired approach with manifold learning

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Face recognition: a convolutional neural-network approach

IEEE Transactions on Neural Networks
Describing people: A poselet-based approach to attribute classification

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
3D Convolutional Neural Networks for Human Action Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a discriminatively-trained convolutional neural network for gender classification of pedestrians. Convolutional neural networks are hierarchical, multilayered neural networks which integrate feature extraction and classification in a single framework. Using a relatively straightforward architecture and minimal preprocessing of the images, we achieved 80.4% accuracy on a dataset containing full body images of pedestrians in both front and rear views. The performance is comparable to the state-of-the-art obtained by previous methods without relying on using hand-engineered feature extractors.