Abstract
Local feature descriptors play an important role in facial expression recognition. Local Binary Pattern (LBP) only considers the signal information of the difference between the gray value of the center pixel and the neighbor pixel. It does not take the magnitude information into consideration and has poor robustness. Local Mapped Pattern (LMP) is not ideal for discriminating differences between different textures and experimental result is not excellent. This paper proposes a novel feature descriptor based on gray-level difference mapping, called Local Double Binary Mapped Pattern (LDBMP).This new approach is an improvement over the previous LBP and LMP, not only retains the advantages of LBP and LMP but also preserves the information of magnitude and captures nuances that occur in the image. In our experiments, the new descriptor performs favorably.
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Acknowledgements
This research has been partially supported by National Natural Science Foundation of China (Grant No. 61672202, 61502141), State Key Program of NSFC-Shenzhen Joint Foundation (Grant No. U1613217) and State Key Program of National Natural Science of China (61432004).
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Yang, C., Hu, M., Zheng, Y., Wang, X., Gao, Y., Wu, H. (2018). Facial Expression Recognition Based on Local Double Binary Mapped Pattern. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_45
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