Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …

Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

In the recent times, with an increase in human identity thefts, the fingerprint-based biometric
systems play a significant role in secured authentication and access restrictions. The …

Anomaly detection is an essential task for different fields in the real world. The imbalanced
data and lack of labels make the task challenging. Deep learning models based on …

Contactless fingerprint recognition is known for its high user comfort and low hygienic
concerns. However, contactless fingerprint recognition, especially in mobile and …

To achieve reliable and automatic anomaly detection (AD) for large equipment such as
liquid rocket engine (LRE), multisource data are commonly manipulated in deep learning …

Recently, biometric verification systems have been widely used in security applications with
multi-level authentication. Existing machine learning approaches were utilized to develop …

The fingerprint-based authentication systems are being extensively deployed as security
tool for providing access to the critical Cyber Physical Systems (CPS). However, this rapid …

Recently, deep learning (DL) has achieved great success in biometric recognition. The
application of DL has also led to a high demand for biometric data. However, as people …

In this paper, we propose an outlier-detection algorithm for detecting network traffic
anomalies based on a clustering algorithm and an autoencoder model. The BIRCH …