Advances in Pattern Analysis for Identity Recognition and Verification II
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 1798
Special Issue Editor
Interests: computational intelligence; machine learning; computer vision; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Currently, we are witnesses of the fourth industrial revolution, also known as Industry 4.0 in Europe, Society 5.0 in Japan and the Industrial Internet of Things (IIT) in the USA. The main characteristics of this new living model are the massive interaction of humans with machines (smartphones, computers, social robots, etc.) and the generation, storing and processing of big data using A.I. algorithms and the Internet of Things (IoT).
In this context, there is a continuously increasing need to identify humans and entities in general, by analyzing their patterns. For example, in the coming years, humans will interact with ATM machines without using debit cards but with their biometrics.
Although significant progress has been reported recently in the field of biometrics by incorporating deep learning models, the task of identifying someone under varying conditions is still open. For example, human identification in the wild or using only a few samples of data are cases that merit more attention and research towards better analysis of the patterns describing the identities.
This Special Issue aims to summarize the recent advances in extracting and analyzing patterns that are able to identify the owner of them. Topics may include but are not limited to:
- Face recognition/verification;
- Iris recognition/verification;
- Fingerprint recognition/verification;
- Ear recognition/verification;
- Vein recognition/verification;
- Gait recognition/verification;
- EEG recognition/verification;
- Behavioral biometrics;
- Multimodal and adaptive biometric systems;
- Adversarial machine learning for biometric systems;
- Feature extraction;
- Storing and processing big biometric data;
- New biometric systems architectures;
- New benchmark datasets;
- New applications.
Prof. Dr. George A. Papakostas
Guest Editor
Manuscript Submission Information
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Keywords
- biometrics
- identity recognition/verification
- feature extraction
