Abstract
With the development of biomedicine, more and more computational models inspired by biological vision system have emerged. The HMAX model (Hierarchical Model and X) based on the visual pathway of the cerebral cortex is one of the classic calculation models. The model has achieved remarkable results in several coarse-grained recognition tasks. In this paper, the use of this model in fine-grained attribute prediction is studied. We propose a new image patch extraction method consisting of the distribution characteristics of face attributes. Graph convolutional neural networks are used to learn the relationship between attributes, which is embedded in the HMAX features. Compared with traditional HMAX, our prediction model performs better on face attribute estimation, with an improvement of 3%.
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Acknowledgments
The work is supported by the National Natural Science Foundation of China under Grant No.: 61976132, U1811461 and the Natural Science Foundation of Shanghai under Grant No.: 19ZR1419200.
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Deng, Z., Fang, Y., Zhang, Y. (2021). Face Attribute Estimation with HMAX-GCNet Model. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_43
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DOI: https://doi.org/10.1007/978-3-030-86608-2_43
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