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Accurate Face and Human Detection Using Hybrid Local Transform Features

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Book cover Theory and Applications of Smart Cameras

Part of the book series: KAIST Research Series ((KAISTRS))

Abstract

We propose two novel local transform features: local gradient patterns (LGP) and binary histograms of oriented gradients (BHOG). LGP assigns one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients and zero otherwise, which makes the local intensity variations along the edge components robust. BHOG assigns one if the histogram bin has a higher value than the average value of the total histogram bins, and zero otherwise, which makes the feature computation time fast due to no further post-processing and SVM classification. We also propose a hybrid feature that combines several local transform features by AdaBoost feature selection method where the best local transform feature among several local transform features (LBP, LGP, and BHOG), which has the lowest classification error, is sequentially selected until we obtain the required classification performance. This hybridization makes the face and human detection robust to the global illumination change by LBP, the local intensity change by LGP, and the local pose change by BHOG, which improves the detection performance considerably. We apply the proposed local transform features and the hybrid feature to the face detection problem using MIT+CMU and FDDB face database and the human detection problem using INRIA human database. The experimental results show that the proposed LGP and BHOG features attain accurate detection performance and fast computation time, respectively, and the hybrid feature provides a considerable improvement of face detection and human detection.

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Notes

  1. 1.

    See database(http://imlab.postech.ac.kr/faceDB/FDD06/FDD06.html.

  2. 2.

    See http://vis-www.cs.umass.edu/fddb/results.html.

  3. 3.

    See http://cbcl.mit.edu/software-datasets/PedestrianData.html.

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Acknowledgements

This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pohang University of Science and Technology, San31, Hyoja-dong, Nam-gu, Gyeongbuk, Pohang, 790-784, Korea

    Daijin Kim

  2. StradVision, Rm 106, Ji-Gok Research Building, San 31, Hyoja-dong, Nam-gu, Gyeongbuk, Pohang, 790-784, Korea

    Bongjin Jun

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  1. Daijin Kim
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Correspondence to Daijin Kim .

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Editors and Affiliations

  1. Electrical Engineering Center for Integrated Smart Sensors, KAIST, Daejoen, Korea, Republic of (South Korea)

    Chong-Min Kyung

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Kim, D., Jun, B. (2016). Accurate Face and Human Detection Using Hybrid Local Transform Features. In: Kyung, CM. (eds) Theory and Applications of Smart Cameras. KAIST Research Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9987-4_8

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  • DOI: https://doi.org/10.1007/978-94-017-9987-4_8

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  • Online ISBN: 978-94-017-9987-4

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