Fractional Face Gratitude by towing items landscapes and Active Feature Matching

Preeti A Pawal; Pranali S Kandhare; Pratiksha P Shendkar; Snehal S Ubale; Prof S P Jadhav

Fractional Face Gratitude by towing items landscapes and Active Feature Matching

Author(s)	Preeti A Pawal, Pranali S Kandhare, Pratiksha P Shendkar, Snehal S Ubale, Prof S P Jadhav
Country	India
Abstract	Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes. DFM does not require prior position information of partial faces against a holistic face. By sharing computation, the feature maps are calculated from the entire input image once, which yields a significant speedup. Experimental results demonstrate the effectiveness and advantages of DFM in comparison with state-of-the-art PFR methods on several partial face databases, including CAISA-NIR-Distance, CASIA-NIR-Mobile, and LFW databases.
Keywords	Machine learning, deep Convolutional neural network, classification
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2020
Published On	2020-05-23
Cite This	Fractional Face Gratitude by towing items landscapes and Active Feature Matching - Preeti A Pawal, Pranali S Kandhare, Pratiksha P Shendkar, Snehal S Ubale, Prof S P Jadhav - IJFMR Volume 2, Issue 3, May-June 2020.

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Fractional Face Gratitude by towing items landscapes and Active Feature Matching

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