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- W3120417058 abstract "The task of face identifications has been a topic of research from the last very decades. The accuracy of the model has always been a mere challenge among researchers. This article proposes a unified model for facial identification in a noisy scenario. The model deploys a Multiple Task Cascaded Neural Network (MTCNN) deep learning model to detect the human face present in test images. The challenge of model performance in a noisy environment is encountered using non-local means denoising algorithm. The denoising is performed at the feeding end of MTCNN network to boost its performance in a noisy scenario. The experimental results justify the proposed model outperforming the prior deep learning strategies for face identification. The model achieves an accuracy of eighty percent in the Gaussian noise whose variance ranging between a hundred and two hundred." @default.
- W3120417058 created "2021-01-18" @default.
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- W3120417058 date "2021-01-01" @default.
- W3120417058 modified "2023-09-25" @default.
- W3120417058 title "A Unified Model for Face Detection Using Multiple Task Cascaded Neural Network Prepended with Non-local Mean Denoising Algorithm" @default.
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- W3120417058 doi "https://doi.org/10.1007/978-981-15-8530-2_34" @default.
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