Deep learning for biometrics: A survey
K Sundararajan, DL Woodard - ACM Computing Surveys (CSUR), 2018 - dl.acm.org
In the recent past, deep learning methods have demonstrated remarkable success for
supervised learning tasks in multiple domains including computer vision, natural language …
supervised learning tasks in multiple domains including computer vision, natural language …
A perspective analysis of handwritten signature technology
Handwritten signatures are biometric traits at the center of debate in the scientific
community. Over the last 40 years, the interest in signature studies has grown steadily …
community. Over the last 40 years, the interest in signature studies has grown steadily …
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
Mobile technologies offer new opportunities for prospective, high resolution monitoring of
long-term health conditions. The opportunities seem of particular promise in psychiatry …
long-term health conditions. The opportunities seem of particular promise in psychiatry …
Deep learning in diverse intelligent sensor based systems
Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …
virtually all fields of science and engineering. The increasing complexity and the large …
DeFFusion: CNN-based continuous authentication using deep feature fusion
Smartphones have become crucial and important in our daily life, but the security and
privacy issues have been major concerns of smartphone users. In this article, we present …
privacy issues have been major concerns of smartphone users. In this article, we present …
Embedding and learning with signatures
A Fermanian - Computational Statistics & Data Analysis, 2021 - Elsevier
Sequential and temporal data arise in many fields of research, such as quantitative finance,
medicine, or computer vision. A novel approach for sequential learning, called the signature …
medicine, or computer vision. A novel approach for sequential learning, called the signature …
Handwritten signature verification using shallow convolutional neural network
Handwritten signatures are an undeniable and unique way to prove the identity of persons.
Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral …
Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral …
Signature identification and verification techniques: state-of-the-art work
Signature identification and verification are some of the biometric systems used for personal
identification. Signatures can be considered as authentication of an individual by the …
identification. Signatures can be considered as authentication of an individual by the …
Recurrent adaptation networks for online signature verification
Online signature verification remains a challenging task owing to large intra-individual
variability. To tackle this problem, in this paper, we propose to use recurrent neural networks …
variability. To tackle this problem, in this paper, we propose to use recurrent neural networks …
Improving handwritten mathematical expression recognition via similar symbol distinguishing
Z Li, X Wang, Y Liu, L Jin, Y Huang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Handwritten mathematical expression recognition (HMER) is an essential task in the OCR
community, which consists of two sub-tasks, ie, symbol recognition and structure parsing …
community, which consists of two sub-tasks, ie, symbol recognition and structure parsing …