Separable Batch Normalization for Robust Facial Landmark Localization
with Cross-protocol Network Training
- URL: http://arxiv.org/abs/2101.06663v1
- Date: Sun, 17 Jan 2021 13:04:06 GMT
- Title: Separable Batch Normalization for Robust Facial Landmark Localization
with Cross-protocol Network Training
- Authors: Shuangping Jin, Zhenhua Feng, Wankou Yang, Josef Kittler
- Abstract summary: A big, diverse and balanced training data is the key to the success of deep neural network training.
A small dataset without diverse and balanced training samples cannot support the training of a deep network effectively.
This paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization.
- Score: 41.82379935715916
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A big, diverse and balanced training data is the key to the success of deep
neural network training. However, existing publicly available datasets used in
facial landmark localization are usually much smaller than those for other
computer vision tasks. A small dataset without diverse and balanced training
samples cannot support the training of a deep network effectively. To address
the above issues, this paper presents a novel Separable Batch Normalization
(SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust
facial landmark localization. Different from the standard BN layer that uses
all the training data to calculate a single set of parameters, SepBN considers
that the samples of a training dataset may belong to different sub-domains.
Accordingly, the proposed SepBN module uses multiple sets of parameters, each
corresponding to a specific sub-domain. However, the selection of an
appropriate branch in the inference stage remains a challenging task because
the sub-domain of a test sample is unknown. To mitigate this difficulty, we
propose a novel attention mechanism that assigns different weights to each
branch for automatic selection in an effective style. As a further innovation,
the proposed CNT strategy trains a network using multiple datasets having
different facial landmark annotation systems, boosting the performance and
enhancing the generalization capacity of the trained network. The experimental
results obtained on several well-known datasets demonstrate the effectiveness
of the proposed method.
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