A Preliminary Study on Non-Intrusive User Authentication Method Using Smartphone Sensors

2013 ◽  
Vol 284-287 ◽  
pp. 3270-3274 ◽  
Author(s):  
Chien Cheng Lin ◽  
Chin Chun Chang ◽  
De Ron Liang ◽  
Ching Han Yang
Keyword(s):  
Error Rate ◽  
User Authentication ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Behavioral Biometrics ◽  
Reported Study ◽  
Smartphone Sensors ◽  
Preliminary Study ◽  
Orientation Sensor

This paper proposes a non-intrusive authentication method based on two sensitive apparatus of smartphones, namely, the orientation sensor and the touchscreen. We have found that these two sensors are capable of capturing behavioral biometrics of a user while the user is engaged in relatively stationary activities. The experimental results with respect to two types of flick operating have an equal error rate of about 3.5% and 5%, respectively. To the best of our knowledge, this work is the first publicly reported study that simultaneously adopts the orientation sensor and the touchscreen to build an authentication model for smartphone users. Finally, we show that the proposed approach can be used together with existing intrusive mechanisms, such as password and/or fingerprints, to build a more robust authentication framework for smartphone users.

scholarly journals Combining Cryptography with EEG Biometrics

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Robertas Damaševičius ◽  
Rytis Maskeliūnas ◽  
Egidijus Kazanavičius ◽  
Marcin Woźniak
Keyword(s):  
Error Rate ◽  
User Authentication ◽  
Discrete Logarithm ◽  
Security Analysis ◽  
Experimental Results ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Eeg Data ◽  
Authentication System ◽  
Biometric Cryptosystem

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

Misuse Detection for Mobile Devices Using Behaviour Profiling

2011 ◽  
Vol 1 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Fudong Li ◽  
Nathan Clarke ◽  
Maria Papadaki ◽  
Paul Dowland
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Text Message ◽  
Modern Society ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Misuse Detection ◽  
General Application ◽  
Point Of Entry ◽  
Application Specific

Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

Palm Vein Recognition Algorithm Using Curvelet and Wavelet

2013 ◽  
Vol 710 ◽  
pp. 655-659
Author(s):  
Zhi Xian Jiu ◽  
Qiang Li
Keyword(s):  
Wavelet Transform ◽  
Error Rate ◽  
Minimum Distance ◽  
Image Database ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Vein Recognition ◽  
Palm Vein

In this paper we report on a curvelet and wavelet based palm vein recognition algorithm. Using our palm vein image database, we employed minimum distance classifier to test the performance of the system. Experimental results show that the algorithm based on cuvelet transform can reach equal error rate of 1.7%, and the algorithm based on wavelet transform can only reach equal error rate of 2.3%, indicating that the curvelet based palm vein recognition system improves representation.

scholarly journals Speaker Verification Employing Combinations of Self-Attention Mechanisms

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2201
Author(s):  
Ara Bae ◽  
Wooil Kim
Keyword(s):  
Error Rate ◽  
Speaker Recognition ◽  
Speaker Verification ◽  
Recognition Performance ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Combination Method ◽  
Noisy Environments ◽  
Equal Error Rate ◽  
Verification System

One of the most recent speaker recognition methods that demonstrates outstanding performance in noisy environments involves extracting the speaker embedding using attention mechanism instead of average or statistics pooling. In the attention method, the speaker recognition performance is improved by employing multiple heads rather than a single head. In this paper, we propose advanced methods to extract a new embedding by compensating for the disadvantages of the single-head and multi-head attention methods. The combination method comprising single-head and split-based multi-head attentions shows a 5.39% Equal Error Rate (EER). When the single-head and projection-based multi-head attention methods are combined, the speaker recognition performance improves by 4.45%, which is the best performance in this work. Our experimental results demonstrate that the attention mechanism reflects the speaker’s properties more effectively than average or statistics pooling, and the speaker verification system could be further improved by employing combinations of different attention techniques.

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

scholarly journals Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

2019 ◽  
Vol 13 ◽  
pp. 174830261984578 ◽  
Author(s):  
Yapin Wang ◽  
Yiping Cao
Keyword(s):  
Error Rate ◽  
Blood Smear ◽  
Imaging System ◽  
Experimental Results ◽  
Segmentation Method ◽  
Microscopic Imaging ◽  
Distance Error ◽  
Segmentation Accuracy ◽  
Different Color ◽  
Nucleus Segmentation

The accuracy of the leukocyte nucleus segmentation is an important preprocessing step in the leukocyte automatic analysis. However, different dyeing conditions or different illumination conditions may cause capturing different color leukocyte images in microscopic imaging system, which will result in the over-segmentation or under-segmentation of the leukocyte nucleus. A leukocyte nucleus segmentation method based on enhancing the saliency of the saturation component is proposed. While applying the set of calibration offset values [Formula: see text], [Formula: see text], and [Formula: see text] of the red (R), green (G), and blue (B) chrominance value on the blood smear microscopic images, it can enhance the saliency of the saturation component and the saliency of the leukocyte nucleus region increases the most obviously. The leukocyte nuclei are then segmented using Otsu’s histogram thresholding method. The experimental results show that the proposed algorithm outperforms the related algorithms in segmentation accuracy, over-segmentation rate, error rate, and relative distance error. It improves the accuracy, robustness, and universality further.

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