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Improved Face Verification with Simple Weighted Feature Combination

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Part of the Communications in Computer and Information Science book series (CCIS,volume 772)

Abstract

Since the appearance of deep learning, face verification (FV) has made great progress with large scale datasets, well-designed networks, new loss functions, fusion of models and metric learning methods. However, incorporating all these methods obviously takes a lot of time both at training and testing stages. In this paper, we just select training images randomly without any clean and alignment procedure. Then we propose a simple weighted average method which combines features of the last two layers with different weights on the modified VGGNet, named as CB-VGG. It is significantly reducing the complexity of time that one model can be treated as two models. LMNN is used as a post-processing procedure to improve the discrimination of the combined features. Our experiments show relatively competitive results on LFW, CFP, and CACD datasets.

Keywords

  • Face verification
  • Deep learning
  • Weighted average method
  • LMNN metric learning

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Notes

  1. 1.

    Because of lacking of data, we couldn’t report the work of Sankarana et al. [26].

  2. 2.

    Due to the restrictions of memory and time, we don’t conduct an experiment on 100 K dataset with multiple crop. The number of images is less than original images which is due to failing detection.

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Acknowledgements

This work was supported by Natural Science Foundation of Shanghai (No. 17ZR1431500).

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Correspondence to Xinyu Zhang .

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Zhang, X., Zhu, J., You, M. (2017). Improved Face Verification with Simple Weighted Feature Combination. In: , et al. Computer Vision. CCCV 2017. Communications in Computer and Information Science, vol 772. Springer, Singapore. https://doi.org/10.1007/978-981-10-7302-1_2

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  • DOI: https://doi.org/10.1007/978-981-10-7302-1_2

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