2 research outputs found
A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition
Deep learning-based palmprint recognition algorithms have shown great
potential. Most of them are mainly focused on identifying samples from the same
dataset. However, they may be not suitable for a more convenient case that the
images for training and test are from different datasets, such as collected by
embedded terminals and smartphones. Therefore, we propose a novel Joint Pixel
and Feature Alignment (JPFA) framework for such cross-dataset palmprint
recognition scenarios. Two stage-alignment is applied to obtain adaptive
features in source and target datasets. 1) Deep style transfer model is adopted
to convert source images into fake images to reduce the dataset gaps and
perform data augmentation on pixel level. 2) A new deep domain adaptation model
is proposed to extract adaptive features by aligning the dataset-specific
distributions of target-source and target-fake pairs on feature level. Adequate
experiments are conducted on several benchmarks including constrained and
unconstrained palmprint databases. The results demonstrate that our JPFA
outperforms other models to achieve the state-of-the-arts. Compared with
baseline, the accuracy of cross-dataset identification is improved by up to
28.10% and the Equal Error Rate (EER) of cross-dataset verification is reduced
by up to 4.69%. To make our results reproducible, the codes are publicly
available at http://gr.xjtu.edu.cn/web/bell/resource.Comment: 12 pages, 7 figure
Towards Efficient Unconstrained Palmprint Recognition via Deep Distillation Hashing
Deep palmprint recognition has become an emerging issue with great potential
for personal authentication on handheld and wearable consumer devices. Previous
studies of palmprint recognition are mainly based on constrained datasets
collected by dedicated devices in controlled environments, which has to reduce
the flexibility and convenience. In addition, general deep palmprint
recognition algorithms are often too heavy to meet the real-time requirements
of embedded system. In this paper, a new palmprint benchmark is established,
which consists of more than 20,000 images collected by 5 brands of smart phones
in an unconstrained manner. Each image has been manually labeled with 14 key
points for region of interest (ROI) extraction. Further, the approach called
Deep Distillation Hashing (DDH) is proposed as benchmark for efficient deep
palmprint recognition. Palmprint images are converted to binary codes to
improve the efficiency of feature matching. Derived from knowledge
distillation, novel distillation loss functions are constructed to compress
deep model to further improve the efficiency of feature extraction on light
network. Comprehensive experiments are conducted on both constrained and
unconstrained palmprint databases. Using DDH, the accuracy of palmprint
identification can be increased by up to 11.37%, and the Equal Error Rate (EER)
of palmprint verification can be reduced by up to 3.11%. The results indicate
the feasibility of our database, and DDH can outperform other baselines to
achieve the state-of-the-art performance. The collected dataset and related
source codes are publicly available at http://gr.xjtu.edu.cn/web/bell/resource.Comment: 13 pages, 8 figures, to access database, see
http://gr.xjtu.edu.cn/web/bell/resourc