Footprint Identification: Review of an Emerging Biometric Trait

2021 ◽  
Vol 397 (1) ◽  
pp. 2000246
Author(s):  
Rohit Khokher ◽  
Ram Chandra Singh
Keyword(s):  
Biometric Trait

Integrated Approach for Sclera Recognition and Eye Gaze Detection

2018 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Snehal S. Rajole ◽  
J. V. Shinde
Keyword(s):  
Near Infrared ◽  
Eye Gaze ◽  
Integrated Approach ◽  
Error Rates ◽  
Infrared Images ◽  
Scaling Method ◽  
Extensive Simulation ◽  
Sclera Recognition ◽  
Gaze Detection ◽  
Biometric Trait

In this paper we proposed unique technique which is adaptive to noisy images for eye gaze detection as processing noisy sclera images captured at-a-distance and on-the-move has not been extensively investigated. Sclera blood vessels have been investigated recently as an efficient biometric trait. Capturing part of the eye with a normal camera using visible-wavelength images rather than near infrared images has provoked research interest. This technique involves sclera template rotation alignment and a distance scaling method to minimize the error rates when noisy eye images are captured at-a-distance and on-the move. The proposed system is tested and results are generated by extensive simulation in java.

Biometric authentication system using EEG biometric trait – A review

2021 ◽  
Author(s):  
Fatin Atiqah Rosli ◽  
Saidatul Ardeenawatie Awang ◽  
Azian Azamimi Abdullah ◽  
Mohammad Shahril Salim
Keyword(s):  
Biometric Authentication ◽  
Authentication System ◽  
Biometric Trait

scholarly journals Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junfeng Yang ◽  
Yuwen Huang ◽  
Fuxian Huang ◽  
Gongping Yang
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
K Nearest Neighbor ◽  
Biometric Recognition ◽  
Recognition Model ◽  
The Third ◽  
Model Based ◽  
Current State ◽  
Biometric Trait

Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy.

An Application of an EM Algorithm for Skew Detection of Signatures in Text Images

2019 ◽  
Vol 9 (4) ◽  
pp. 49-58
Author(s):  
Rajesh T. M. ◽  
Kavyashree Dalawai
Keyword(s):  
Feature Extraction ◽  
Em Algorithm ◽  
Skew Detection ◽  
The Em Algorithm ◽  
Real World Applications ◽  
Novel Method ◽  
Verification Process ◽  
Two Stages ◽  
Text Images ◽  
Biometric Trait

For security purposes of important documents and transactions in real world applications, we generally use biometric techniques for the authentication and validation of a person. If one has to achieve accurate results in the identification and verification process using a signature in text images as a biometric trait, we need to remove the skew of the signature in text images. In the preprocessing stage many phases are being carried out, among these phases, the signature in the text image, skew detection is the most significant phase, because these deskewed results will be used as one of the features in the feature extraction phase to identify and verify the signature. In this article we are proposing a novel method for skew detection of the signatures in text images using an estimation and maximization (EM) algorithm which is efficient and fast. The EM algorithm sequentially works in two stages, the combination of estimation (E-step) and the maximization (M-step) which helps in detection of the skew in skewed signatures in text image accurately.

scholarly journals EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 6 ◽  
Author(s):  
Ying Zeng ◽  
Qunjian Wu ◽  
Kai Yang ◽  
Li Tong ◽  
Bin Yan ◽  
...  
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Visual Presentation ◽  
Specific Model ◽  
Identity Authentication ◽  
Event Related Potential ◽  
Time Interval ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Biometric Trait ◽  
Over Time

Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time.

scholarly journals Masquerade Attack Analysis for Secured Face Biometric System

2021 ◽  
Vol 10 (2) ◽  
pp. 225-232
Author(s):  
Sanjeeva Kumar M. Hatture ◽  
◽  
Shweta Policepatil ◽  
Keyword(s):  
Hybrid Approach ◽  
Personal Identification ◽  
Biometric System ◽  
Biometric Data ◽  
Left Turn ◽  
Liveness Detection ◽  
Identity Based ◽  
Recognition Systems ◽  
Biometric Trait ◽  
Face Biometric

Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.

From Single Biometrics to Multi-Biometrics

2011 ◽  
pp. 254-272
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Performance Evaluation ◽  
Research And Development ◽  
Real World ◽  
System Performance ◽  
Biometric System ◽  
The Past ◽  
Biometric Systems ◽  
Real World Applications ◽  
Biometric Trait

In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single biometric trait may not satisfy the demand of some real-world applications. Diversity of biometric traits also means that they may have different performance such as accuracy and reliability. Multi-biometric applications emerging in recent years are a big progress of biometrics. They can overcome some shortcomings of the single biometric system and can perform well in improving the system performance. In this chapter we describe a number of definitions on biometrics, categories and fusion strategies of multi-biometrics as well as the performance evaluation on the biometric system. The first section of this chapter describes some concepts, motivation and justification of multi-biometrics. Section 12.2 provides some definitions and notations of biometric and multi-biometric technologies. Section 12.3 is mainly related to performance evaluation of various types of biometric systems. Section 12.4 briefly presents research and development of multi-biometrics.

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