Multimodal person detection system - Barello, Philip; Hossain, Shafaeat (Multimedia Tools and Applications, 2021)
Person detection is often critical for personal safety, property protection, and national security. Most person detection technologies implement unimodal classification, making predictions based on a single sensor data modality, which is most often vision. There are many ways to defeat unimodal person detectors, and many more reasons to ensure technologies responsible for detecting the presence of a person are accurate and precise. In this paper, we design and implement a multimodal person detection system which can acquire data from multiple sensors and detect persons based on a variety of unimodal classifications and multimodal fusions. We present two methods of generating system-level predictions: (1) device perspectives which makes a final decision based on multiple device-level predictions and (2) system perspectives which combines data samples from multiple devices into a single data sample and then makes a decision. Our experimental results show that system-level predictions from system perspectives are generally more accurate than system-level predictions from device perspectives. We achieve an accuracy of 100%, zero false positive rate and zero false negative rate with fusion of system perspectives motion and distance data. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
1 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85099450439
Keywords: Design and implements; False negative rate; False positive rates; Forecasting; Generating system; Multi-modal fusion; Multiple devices; National security; Person detection; Property protection; dept.csc; peer-reviewed

Effectiveness of deep learning on serial fusion based biometric systems - Edwards, Tiffanie; Hossain, Shafaeat (IEEE Transactions on Artificial Intelligence, 2021)
12 citations (Crossref) [2023-10-31] https://dblp.uni-trier.de/db/journals/tai/tai2.html#EdwardsH21
Keywords: dept.csc; peer-reviewed

Enhancing performance and user convenience of multi-biometric verification systems - Hossain, Shafaeat; Phoha, Vir V. (Pattern Analysis and Applications, 2021)
A multi-biometric verification system lowers the verification errors by fusing information from multiple biometric sources. Information can be fused in parallel or serial modes. While parallel fusion gives a higher accuracy, it may suffer from a serious problem of taking a longer verification time. Serial fusion can alleviate this problem by allowing the users to submit a subset of the available biometric characteristics. Unfortunately, several studies show that serial fusion may not reach the level of accuracy of parallel fusion. In this paper, we propose a fusion framework which combines the advantages of both parallel and serial fusion. The core of the framework is a new concept of “confident reject region” which incurs nearly zero verification error. We evaluate our framework by performing experiments on two multi-biometric verification systems built with NIST biometric scores set release 1. The experimental results show that our framework achieves a lower equal error rate and takes a shorter verification time than standard parallel fusion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
2 citations (Crossref) [2023-10-31] https://experts.syr.edu/en/publications/enhancing-performance-and-user-convenience-of-multi-biometric-ver ; https://dblp.uni-trier.de/db/journals/paa/paa24.html#HossainP21
Keywords: dept.csc; peer-reviewed

Exploring capacitive swipe gesture for user authentication using a new large dataset - K.C., Kiran; Hossain, Shafaeat; Haberfeld, Carl (Computers & Security, 2025)
Keywords: Artificial intelligence; Authentication; Authentication (law); Biometrics; Capacitive sensing; Capacitive swipe gesture; Computer science; Computer security; Gesture; Operating system; Smartphone security; SwIPe; Touchscreen; dept.csc

Analyzing the sentiment correlation between regular tweets and retweets - Chen, Jundong; Hossain, Shafaeat; Zhang, Huan (Social Network Analysis and Mining, 2020)
Retweeting is an important way of information propagation on Twitter. In this paper, we investigate the sentiment correlation between regular tweets and retweets. We anticipate our investigation sheds a light on how the sentiment of regular tweets impacts the retweets of different sentiments. We propose a method for measuring the sentiment of tweets. We categorize the Twitter users into different groups by different norms, which are the follower count, the betweenness connectivity, a combination of follower count and betweenness centrality,and the amount of tweets. Then, we calculate the sentiment correlation for different groups to examine the influential factors for retweeting a message with a certain sentiment.We find that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation. The users with medium-level followers_count show the highest sentiment correlation compared to the low-level and high-level followers_count. After combining the two factors of followers_count and betweenness centrality, we discover that specifically at low-level betweenness centrality the users with medium-level followers_count have the highest sentiment correlation. Our last observation is that the difference for correlation coefficients exists between different types of users. Our study on the sentiment correlation provides instructional information for modeling information propagation in human society. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
21 citations (Crossref) [2023-10-31] tex.ids: chenAnalyzingSentimentCorrelation2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85078901417 type: Article
Keywords: Betweenness; Betweenness centrality; Correlation coefficient; Human society; Influential factors; Information dissemination; Information propagation; Instructional information; Learning systems; Machine learning; Model informations; Sentiment analysis; Social network; Social networking (online); Twitter classification; dept.csc; peer-reviewed

Open code biometric tap pad for smartphones - Haberfeld, Carl; Hossain, Shafaeat; Lancor, Lisa (Journal of Information Security and Applications, 2021)
Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of “open code” biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as (1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the “usual” finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
1 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Decision trees; Extra sensors; Fasteners; Index finger; Learning algorithms; Mobile security; Neural networks; Open codes; Secret code; Security Practice; Security systems; Shoulder surfing; Smartphone securities; Smartphones; Support vector machines; Typing behaviors; dept.csc; peer-reviewed

The effectiveness of zoom touchscreen gestures for authentication and identification and its changes over time - Wang, Leran; Hossain, Shafaeat; Pulfrey, Joshua; Lancor, Lisa (Computers & Security, 2021)
This paper focuses on how zoom touchscreen gestures can be used to continuously authenticate and identify smartphone users. The zoom gesture is critically under-researched as a behavioral biometric despite richness of data found in this gesture. Furthermore, analysing how the zoom gesture performs over time is a novel line of inquiry. Zoom samples from three different data collection sessions were sourced. In these sessions, each participant zoomed in and out on three images. Eighty-five features were extracted from each gesture. The classification models used were Support Vector Machine (SVM), Random Forest (RF), and K-nearest Neighbor (KNN). The best authentication performance of AUC 0.937 and EER 10.6% were achieved using the SVM classifier. The best identification performance of 65.5% accuracy, 69.6% precision, and 67.9% recall were achieved using the RF classifier. In terms of stability over time, SVM proved to be the most stable classifier, with an AUC degradation of only 0.007 after two weeks had elapsed. This analysis proves that zoom gestures demonstrate promise for use in continuous smartphone authentication and identification applications. © 2021 Elsevier Ltd
6 citations (Crossref) [2023-10-31]
Keywords: Authentication; Behavioural Biometric; Biometrics; Change-over time; Classification models; Data collection; Decision trees; Identification; Nearest neighbor search; Performance; Smart phones; Smartphones; Support vector machines; Support vectors machine; Touch gesture; Zoom; dept.csc; peer-reviewed

DAAB: Deep authorship attribution in bengali - Dipongkor, Atish Kumar; Islam, Saiful; Kayesh, Humayun; Hossain, Shafaeat; Anwar, Adnan; Rahman, Khandaker Abir; Razzak, Imran (2021 international joint conference on neural networks (IJCNN), 2021)
Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model. © 2021 IEEE.
2 citations (Crossref) [2023-10-31]
Keywords: Authorship attribution; Benchmark datasets; Bengalis; Convolution; Convolutional neural network; Convolutional neural networks; Deep learning; Deep neural networks; Learn+; Plagiarism detection; Sets of features; Stylometry; dept.csc