Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3143–3170 | Cite as

Face detection of golden monkeys via regional color quantization and incremental self-paced curriculum learning

  • Pengfei Xu
  • Songtao Guo
  • Qiguang Miao
  • Baoguo Li
  • Xiaojiang Chen
  • Dingyi Fang


Animal detection plays a very vital role in wildlife protection and many other real life applications. In this paper, we focus on face detection of Golden monkeys who live in Qinling Mountains, Shaanxi province, China, and present a relatively complete face detection algorithm to detect these monkeys’ faces, which mainly includes three parts: the location of the monkeys’ bodies, the detection of th+e suspicious facial skin and the accurate detection of the true faces. Firstly, regional color quantization is proposed to quantize the HSV color space for the nature images with different sizes, and we can get the areas of the monkeys’ bodies according to the color distribution of the monkeys’ hair in the histogram of the quantized color space. Then the areas of suspicious facial skin can be extracted from these areas of the monkeys’ bodies. Further, we propose incremental self-paced curriculum learning (ISPCL) to detect the true monkeys’ faces accurately. In our method, regional color quantization can increase the color differences between the background and the monkeys’ hair, so that the segmented results have fewer background pixels. Besides, the basic idea of the incremental learning is introduced into the training process of SPCL, which is to simulate the process in which human learn something from easy samples to hard ones, this idea is able to improve the performances of face detection. The experimental results demonstrate that the proposed algorithm can locate the monkeys’ bodies in the images with different sizes, and can detect their faces effectively. This research lays a foundation for face recognition and behavior analysis of golden monkeys.


golden monkey face detection regional color quantization incremental self-paced curriculum learning 



The work was jointly supported by the National Natural Science Foundations of China under grant No. 61502387 and 41601353; Natural Science Foundation of Shaanxi Province, under grant No.2016JQ6029; The 59th China’s Post-doctoral Science Fund No. 2016 M592832.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Pengfei Xu
    • 1
  • Songtao Guo
    • 2
  • Qiguang Miao
    • 3
  • Baoguo Li
    • 2
  • Xiaojiang Chen
    • 1
  • Dingyi Fang
    • 1
  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  2. 2.Shaanxi Key Laboratory for Animal ConservationNorthwest UniversityXi’anChina
  3. 3.School of ComputerXidian UniversityXi’anChina

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