Konferenzbeitrag
Deep Domain Adaptation for Face Recognition using images captured from surveillance cameras
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Text/Conference Paper
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Datum
2018
Autor:innen
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Verlag
Köllen Druck+Verlag GmbH
Zusammenfassung
Learning based on convolutional neural networks (CNNs) or deep learning has been a major
research area with applications in face recognition (FR). However, performances of algorithms designed for
FR are unsatisfactory when surveillance conditions severely degrade the test probes. The work presented
in this paper has three contributions. First, it proposes a novel adaptive-CNN architecture of deep learning
refurbished for domain adaptation (DA), to overcome the difference in feature distributions between the
gallery and probe samples. The proposed architecture consists of three components: feature (FM), adaptive
(AM) and classification (CM) modules. Secondly, a novel 2-stage algorithm for Mutually Exclusive Training
(2-MET) based on stochastic gradient descent, has been proposed. The final stage of training in 2-MET
freezes the layers of the FM and CM, while updating (tuning) only the parameters of the AM using a few
probe (as target) samples. This helps the proposed deep-DA CNN to bridge the disparities in the distributions
of the gallery and probe samples, resulting in enhanced domain-invariant representation for efficient deep-DA
learning and classification. The third contribution comes from rigorous experimentations performed on three
benchmark real-world surveillance face datasets with various kinds of degradations. This reveals the superior
performance of the proposed adaptive-CNN architecture with 2-MET training, using Rank-1 recognition rates
and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA.