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Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition

Published: 13 October 2015 Publication History

Abstract

This paper addresses the problem of image features selection for pedestrian gender recognition. Hand-crafted features (such as HOG) are compared with learned features which are obtained by training convolutional neural networks. The comparison is performed on the recently created collection of versatile pedestrian datasets which allows us to evaluate the impact of dataset properties on the performance of features. The study shows that hand-crafted and learned features perform equally well on small-sized homogeneous datasets. However, learned features significantly outperform hand-crafted ones in the case of heterogeneous and unfamiliar (unseen) datasets. Our best model which is based on learned features obtains 79% average recognition rate on completely unseen datasets. We also show that a relatively small convolutional neural network is able to produce competitive features even with little training data.

References

[1]
L. Cao, M. Dikmen, Y. Fu, and T. S. Huang. Gender recognition from body. In ACM MM, Canada, 2008.
[2]
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. CoRR, abs/1405.3531, 2014.
[3]
M. Collins, J. Zhang, P. Miller, and H. Wang. Full body image feature representations for gender profiling. In ICCV, Japan, 2009.
[4]
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, USA, 2005.
[5]
Y. Deng, P. Luo, C. C. Loy, and X. Tang. Pedestrian attribute recognition at far distance. In ACM MM, USA, 2014.
[6]
I. J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet. Multi-digit number recognition from street view imagery using deep convolutional neural networks. CoRR, abs/1312.6082, 2013.
[7]
J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 1982.
[8]
G. E. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. In NIPS Deep Learning Workshop, Canada, 2014.
[9]
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. CoRR, abs/1408.5093, 2014.
[10]
T. Joachims. Making large scale SVM learning practical. 1999.
[11]
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, USA, 2012.
[12]
R. Layne, T. M. Hospedales, S. Gong, et al. Person re-identification by attributes. In BMVC, UK, 2012.
[13]
Y. LeCun and Y. Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 1995.
[14]
D. G. Lowe. Object recognition from local scale-invariant features. In ICCV, Canada, 1999.
[15]
C.-B. Ng, Y.-H. Tay, and B.-M. Goi. A convolutional neural network for pedestrian gender recognition. In ISNN. Springer, 2013.
[16]
T. Ojala, M. Pietik\"ainen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 1996.
[17]
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. CoRR, abs/1403.6382, 2014.
[18]
O. Russakovsky, J. Deng, H. Su, J. Krause, and S. S. et al. ImageNet Large Scale Visual Recognition Challenge. CoRR, abs/1409.0575, 2014.
[19]
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In CVPR, USA, 2014.

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    cover image ACM Conferences
    MM '15: Proceedings of the 23rd ACM international conference on Multimedia
    October 2015
    1402 pages
    ISBN:9781450334594
    DOI:10.1145/2733373
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    Publication History

    Published: 13 October 2015

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    Author Tags

    1. CNN
    2. HOG
    3. image features
    4. pedestrian gender recognition

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    MM '15: ACM Multimedia Conference
    October 26 - 30, 2015
    Brisbane, Australia

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    MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

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    • (2024)Estimation of Fractal Dimension and Segmentation of Body Regions for Deep Learning-Based Gender RecognitionFractal and Fractional10.3390/fractalfract81005518:10(551)Online publication date: 24-Sep-2024
    • (2024)Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning ApproachJournal of The Institution of Engineers (India): Series B10.1007/s40031-023-00966-0105:3(469-482)Online publication date: 10-Feb-2024
    • (2024)A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospectsMultimedia Tools and Applications10.1007/s11042-023-15443-583:1(1965-2005)Online publication date: 1-Jan-2024
    • (2024)Enhancing CNN model classification performance through RGB angle rotation methodNeural Computing and Applications10.1007/s00521-024-10232-z36:32(20259-20276)Online publication date: 1-Nov-2024
    • (2023)Survey of Cross-Modal Person Re-Identification from a Mathematical PerspectiveMathematics10.3390/math1103065411:3(654)Online publication date: 28-Jan-2023
    • (2023)Person Identification from Video Analytic System Using Edge Computing -A Review2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE)10.1109/ICCCEE55951.2023.10424635(1-7)Online publication date: 27-Apr-2023
    • (2023)Age group identification using gaze-guided feature extraction2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)10.1109/GCCE59613.2023.10315305(708-711)Online publication date: 10-Oct-2023
    • (2023)Learning to focus on region-of-interests for pain intensity estimation2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)10.1109/FG57933.2023.10042583(1-6)Online publication date: 5-Jan-2023
    • (2023)ViT-PGC: vision transformer for pedestrian gender classification on small-size datasetPattern Analysis and Applications10.1007/s10044-023-01196-226:4(1805-1819)Online publication date: 26-Sep-2023
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