Italy

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. 


ICPR 2020 MAIN CONFERENCE

We present an approach for driver identification, which is useful in many automotive applications such as safety or comfort functions. Driver identification would also be of great interest to other business models, such as car rental and car-sharing companies. The identification method is based on the driver's physiological state or rather his/her electrocardiogram (ECG) data. For this purpose, we have recorded ECG data of 25 people driving in a simulated environment. To identify a driver, we extend our existing novelty detection by aggregating local features over time. To do so, we extracted features and trained a Gaussian Mixture Model (GMM) to exploit localities present in the recorded sensor data. With novelty detection by aggregating local features, we are smoothing the noisy signal and reducing the dimensionality for further processing in a one-class SVM classification. Based on the output, a decision function decides whether the driver is unknown or well-known, and if the driver is well-known, who of the known driver is it.

Novelty based Driver Identification on RR Intervals from ECG Data

technical paper

Defect Detection of Stainless Steel Plates Using Deep Learning Technology

BiLuNet: A Multi-path Network for Semantic Segmentation on X-ray Images

A Hybrid Machine Learning Approach for Energy Consumption Prediction in Additive Manufacturing

Multiregion multiscale image segmentation with anisotropic diffusion

A Novel Multi-Feature Skeleton Representation for 3D Action Recognition

Refined Semantic Segmentation Network of City Remote Sensing Image

Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN

Blockchain-based iris authentication in order tosecure IoT access and digital money spending

DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

A Novel Ensemble Framework for Face Search

poster

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Novelty based Driver Identification on RR Intervals from ECG Data

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