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Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification

Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification

Sahil Sharma, Vijay Kumar
Copyright: © 2019 |Pages: 17
ISBN13: 9781522578628|ISBN10: 1522578625|EISBN13: 9781522578635
DOI: 10.4018/978-1-5225-7862-8.ch006
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MLA

Sharma, Sahil, and Vijay Kumar. "Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification." Handbook of Research on Deep Learning Innovations and Trends, edited by Aboul Ella Hassanien, et al., IGI Global, 2019, pp. 97-113. https://doi.org/10.4018/978-1-5225-7862-8.ch006

APA

Sharma, S. & Kumar, V. (2019). Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification. In A. Hassanien, A. Darwish, & C. Chowdhary (Eds.), Handbook of Research on Deep Learning Innovations and Trends (pp. 97-113). IGI Global. https://doi.org/10.4018/978-1-5225-7862-8.ch006

Chicago

Sharma, Sahil, and Vijay Kumar. "Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification." In Handbook of Research on Deep Learning Innovations and Trends, edited by Aboul Ella Hassanien, Ashraf Darwish, and Chiranji Lal Chowdhary, 97-113. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7862-8.ch006

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Abstract

Face depth image has been used for occlusion presence and gender prediction by transfer learning. This chapter discusses about the overfitting problem and how augmentation helps overcoming it. Pre-processing of the dataset includes converting a 3D object image into depth image for further processing. Five state-of-the-art 2D deep learning models (e.g., AlexNet, VGG16, DenseNet121, ResNet18, and SqueezeNet) have been discussed along with their architecture. The effect of increasing the number of epochs on the top-1 error rate has been presented in the experimental section. The result section consists of error rates in comparison of with and without augmentation on the datasets.

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