Italy

A novel automatic pose-invariant dental arch extraction and matching framework is developed for 3D dental identification using laser-scanned dental plasters. In our previous attempt [1-5], 3D point-based algorithms have been developed and they have shown a few advantages over existing 2D dental identifications. This study is a continuous effort in developing arch-based algorithms to extract and match dental arch feature in an automatic and pose-invariant way. As best as we know, this is the first attempt at automatic dental arch extraction and matching for 3D dental identification. A Radial Ray Algorithm (RRA) is proposed by projecting dental arch shape from 3D to 2D. This algorithm is fully automatic and fast. Preliminary identification result is obtained by matching 11 postmortem (PM) samples against 200 ante-mortem (AM) samples. 72.7% samples achieved top 5% accuracy. 90.9% samples achieved top 10% accuracy and all 11 samples (100%) achieved top 15.5% accuracy out of the 200-rank list. In addition, the time for identifying a single subject from 200 subjects has been significantly reduced from 45 minutes to 5 minutes by matching the extracted 2D dental arch. Although the extracted 2D arch feature is not as accurate and discriminative as the full 3D arch, it may serve as an important filter feature to improve the identification speed in future investigations.

ICPR 2020

3D Dental Biometrics: Automatic Pose-invariant Dental Arch Extraction and Matching

radial ray algorithm (rra)

human identification

dental arch

3d dental biometrics

feature extraction

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. The recognition accuracy of finger vein recognition systems suffers from different internal and external factors. One of the major problems are misplacements of the finger during acquisition. In particular longitudinal finger rotation poses a severe problem for such recognition systems. The detection and correction of such rotations is a difficult task as typically finger vein scanners acquire only a single image from the vein pattern. Therefore, important information such as the shape of the finger or the depth of the veins within the finger, which are needed for the rotation detection, are not available. This work presents a CNN based rotation detector that is capable of estimating the rotational difference between vein images of the same finger without providing any additional information. The experiments executed not only show that the method delivers highly accurate results, but it also generalizes so that the trained CNN can also be applied on data sets which have not been included during the training of the CNN. Correcting the rotation difference between images using the CNN's rotation prediction leads to EER improvements between 50-260\% for a well-established vein-pattern based method (Maximum Curvature) on four public finger vein databases.

Rotation Detection in Finger Vein Biometrics using CNNs

This work concerns feature selection using supervised
classification on high dimensional datasets. The
classical approach is to project data onto a low dimensional space and classify
by minimizing an appropriate quadratic cost. 
We first introduced a matrix of centers in the definition of this cost.
Moreover, as quadratic costs are not robust to outliers, we proposed instead to use an $\ell_1$ cost (or Huber loss to mitigate overfitting issues).
While control
on sparsity is commonly obtained by adding an $\ell_1$ constraint on the vectorized matrix of weights used for projecting the data,
we propose to enforce structured sparsity. To this end we used constraints that take into account the matrix structure of the data, based either on the nuclear norm, on the $\ell_{2,1}$ norm, or on the $\ell_{1,2}$ norm for which we provide a new projection algorithm.
We optimize simultaneously the projection matrix and the matrix of centers 
with a tailored constrained primal-dual method. The primal-dual framework is general enough to encompass the various robust losses and structured constraints and allows for a convergence analysis.
We demonstrate the effectiveness of this approach on three biological datasets. Our primal-dual method with robust losses, adaptive centers and structured constraints does significantly better than classical methods, both in terms of accuracy and computational time.

Classification and feature selection using a primal-dual method and projection on structured constraints

Sonar operations in littoral environments may be challenging due to an increased probability of false alarms. Machine learning can be used to train classifiers that are able to filter out most of the false alarms automatically, however, this is a time consuming process, with many hyperparameters that need to be tuned in order to yield useful results. In this paper, Bayesian optimization is used to search for good values for some of the hyperparameters, like topology and training parameters, resulting in performance superior to earlier trial-and-error based training. Additionally, we analyze some of the parameters involved in the Bayesian optimization, as well as the resulting hyperparameter values.

Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing methods achieve this objective at the cost of a drop in
inference accuracy in computer vision tasks. To address such a drawback, we propose a framework that leverages knowledge distillation along with customizable block-wise optimization to learn a lightweight CNN structure while preserving better control over the compression-performance tradeoff. Considering specific
resource constraints, e.g., floating-point operations per inference (FLOPs) or model-parameters, our method results in a state of the art network compression while being capable of achieving better inference accuracy. In a comprehensive evaluation, we demonstrate that our method is effective, robust, and consistent with results over a variety of network architectures and datasets, at negligible training overhead. In particular, for the already compact network MobileNet v2, our method offers up to 2× and 5.2× better model compression in terms of FLOPs and model-parameters, respectively, while getting 1.05% better model performance than the baseline 
network.

Compact CNN Structure Learning by Knowledge Distillation


Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.

Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

Matching of Matching-Graphs, A Novel Approach for Graph Classification


A number of methods including image processing
technologies (IPTs) and deep learning methods, have been used to
detect defects in civilian infrastructure. These methods have been
introduced to extract features representing cracks in concrete
surfaces. Inspired by recent advances of a pixel distribution
learning method in background subtraction, we propose a novel
multi-scale deep learning method (MS-DPDL) for concrete crack
detection. The designed CNN network is trained on the dataset
CRACK500 and tested on it for concrete segmentation.
To show good transferability of our proposed model, it is later
tested on the dataset Concrete Crack Images for Classification.
Several existing deep learning methods are used to compare the
performance of the proposed MS-DPDL method. Results show
that our method has good performance and can effectively find
concrete cracks in practical situations.

Multi-Scale Deep Pixel Distribution Learning for Concrete Crack Detection


How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and the quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (total about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature, and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data


Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior “blind” works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In this paper we try to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements.

Are Multiple Cross-Correlation Identities better than just Two? Improving the Estimate of Time Differences-of-Arrivals from Blind Audio Signals

One Class Classification(OCC) is an Extreme case in classification where knowledge of the classifier is limited to only a single class. Given training samples from a class, the one class classifier is expected to reject samples from any outside class. we learn generative features using the one-class data with a generative framework. We augment the learned features with the corresponding reconstruction errors to obtain augmented features. Then, we qualitatively identify a suitable feature distribution that reduces the redundancy in the chosen classifier space. Finally, we force the augmented features to take the form of this distribution using an adversarial framework.

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Rotation Detection in Finger Vein Biometrics using CNNs

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