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Journal articles on the topic 'Verification and identification'

Author: Grafiati
Published: 28 June 2021
Last updated: 12 February 2022

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1

Mesec, Chip. "Fingerprint identification versus verification." Biometric Technology Today 15, no. 9 (September 2007): 7. http://dx.doi.org/10.1016/s0969-4765(07)70157-1.

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2

Mammone, Richard J. "Speaker identification and verification system." Journal of the Acoustical Society of America 101, no. 2 (February 1997): 665. http://dx.doi.org/10.1121/1.419408.

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3

Jia-Ching Wang, Chung-Hsien Yang, Jhing-Fa Wang, and Hsiao-Ping Lee. "Robust Speaker Identification and Verification." IEEE Computational Intelligence Magazine 2, no. 2 (May 2007): 52–59. http://dx.doi.org/10.1109/mci.2007.353420.

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4

Li, Kung-Pu. "Automatic language identification/verification system." Journal of the Acoustical Society of America 104, no. 1 (July 1998): 31. http://dx.doi.org/10.1121/1.424049.

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5

Ciskowski, R. D., C. H. Liu, H. H. Ottesen, and S. U. Rahman. "System Identification: An experimental verification." IBM Journal of Research and Development 31, no. 5 (September 1987): 571–84. http://dx.doi.org/10.1147/rd.315.0571.

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6

Yeatman, Timothy J. "Predictive Biomarkers: Identification and Verification." Journal of Clinical Oncology 27, no. 17 (June 10, 2009): 2743–44. http://dx.doi.org/10.1200/jco.2008.21.5087.

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7

Grudzień, Artur, Marcin Kowalski, and Norbert Pałka. "Thermal Face Verification through Identification." Sensors 21, no. 9 (May 10, 2021): 3301. http://dx.doi.org/10.3390/s21093301.

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This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.
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8

Bensefia, Ameur, Thierry Paquet, and Laurent Heutte. "A writer identification and verification system." Pattern Recognition Letters 26, no. 13 (October 2005): 2080–92. http://dx.doi.org/10.1016/j.patrec.2005.03.024.

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9

Shinozuka, Masanobu, and Roger Ghanem. "Structural System Identification. II: Experimental Verification." Journal of Engineering Mechanics 121, no. 2 (February 1995): 265–73. http://dx.doi.org/10.1061/(asce)0733-9399(1995)121:2(265).

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10

Kinnunen, T., E. Karpov, and P. Franti. "Real-time speaker identification and verification." IEEE Transactions on Audio, Speech and Language Processing 14, no. 1 (January 2006): 277–88. http://dx.doi.org/10.1109/tsa.2005.853206.

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11

Krishnamurthy, Thaiya, Johannes Hewel, Neil J. Bonzagni, Jason Dabbs, Robert L. Bull, and John R. Yates. "Simultaneous identification and verification ofBacillus anthracis." Rapid Communications in Mass Spectrometry 20, no. 13 (2006): 2053–56. http://dx.doi.org/10.1002/rcm.2564.

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12

Inoue, Kazuya, Akira Kobayashi, Naoko Matsunaga, and Tsutomu Tanaka. "Application of Particle Tracking Method to Dispersivity Identification and Its Experimental Verification." Journal of Rainwater Catchment Systems 13, no. 2 (2008): 7–16. http://dx.doi.org/10.7132/jrcsa.kj00004871195.

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13

Lv, Gang, and Shou Wen Fan. "Assembly Design Defects Identification for Mechanical Products Based on Constraint Verification Tree." Advanced Materials Research 694-697 (May 2013): 3159–63. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.3159.

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A methodology for mechanical products assembly design defect identification was presented based on assembly constraint verification tree (ACVT). An identification system framework, which consists of verification tree constructors, modifier, monitor and identification analyzer, was proposed. Modified design parameters were stored into modifier, monitor was employed to supervise design parameters variation in modifier and renew design parameters information in verification tree, identification analyzer decided the start or stop of verification according to stack information in monitor. A prototype system based on above identification system framework was developed, assembly design defects identification results for driving leg of parallel manipulators demonstrate that not only can it enhance identification efficiency for assembly design defects effectively, but also can implement dynamic relating identification for assembly design defects of mechanical products.
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14

KALERA, MEENAKSHI K., SARGUR SRIHARI, and AIHUA XU. "OFFLINE SIGNATURE VERIFICATION AND IDENTIFICATION USING DISTANCE STATISTICS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1339–60. http://dx.doi.org/10.1142/s0218001404003630.

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This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
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15

Epifantsev, B. N., P. S. Lozhnikov, A. E. Sulavko, and S. S. Zhumazhanova. "Identification potential of online handwritten signature verification." Optoelectronics, Instrumentation and Data Processing 52, no. 3 (May 2016): 238–44. http://dx.doi.org/10.3103/s8756699016030043.

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16

Ariyaeeinia, A. M., J. Fortuna, P. Sivakumaran, and A. Malegaonkar. "Verification effectiveness in open-set speaker identification." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 5 (2006): 618. http://dx.doi.org/10.1049/ip-vis:20050273.

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17

Tian, Hui, Xiang Zhang, Long Lan, and Zhigang Luo. "Person re-identification via adaptive verification loss." Neurocomputing 359 (September 2019): 93–101. http://dx.doi.org/10.1016/j.neucom.2019.05.037.

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18

Gorban, Igor I. "Crime automatic speaker verification and identification system." Journal of the Acoustical Society of America 102, no. 5 (November 1997): 3165. http://dx.doi.org/10.1121/1.420769.

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19

Crosswhite, Nate, Jeffrey Byrne, Chris Stauffer, Omkar Parkhi, Qiong Cao, and Andrew Zisserman. "Template adaptation for face verification and identification." Image and Vision Computing 79 (November 2018): 35–48. http://dx.doi.org/10.1016/j.imavis.2018.09.002.

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20

Rusli, Leonard, and Anthony Luscher. "Fastener identification and assembly verification via machine vision." Assembly Automation 38, no. 1 (February 5, 2018): 1–9. http://dx.doi.org/10.1108/aa-08-2016-093.

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Purpose The study aims to evaluate the capability of a machine vision camera and software to recognize fasteners for the purpose of assembly verification. This will enable the current assembly verification system to associate torque verfication with a specific fastener. Design/methodology/approach A small camera is installed at the head of a tool near the socket. The camera is used to capture images surrounding the fastener, and feeding them into machine vision recognition software. By recognizing unique features around the fastener, the fastener can be uniquely identified and therefore verified to be assembled. Additional filtering and multiple frame recognition will improve the reliability of the recognition. Findings The machine vision technology is found to be adequately reliable in identifying fasteners after tuning key threshold parameters and requiring multiple positively recognized frames. The time to verify can be kept around a fraction of a second to prevent impacting assembly speed. Research limitations/implications This experiment was run under simulated assembly line lighting conditions. It also does not explore industrial remote head industrial camera hardware. Practical implications By using a remote-mounted camera in combination with electric tools, a reliable assembly verification system can be used to eliminate torque check processes of critical fasteners, thereby reducing the cost of assembly. Originality/value Currently, assembly verification is done only using the torque values. In automated assembly line, each process might involve fastening multiple fasteners. Using this system, a new level of assembly verification is achieved by recording the assembled fastener and its associated torque.
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21

Kroening, Michael, Yana Salchak, and Dmitriy A. Sednev. "Closure Welds Identification by Means of Ultrasonic Testing." Advanced Materials Research 1040 (September 2014): 933–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1040.933.

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In present paper, possibilities for identification and verification of closure welds were discussed. It might be applied for nonproliferation purposes, where validity and reliability of verification are often crucial issue. Methodology of ultrasonic testing and signal processing procedure were proposed. Through set of experiments, the validity of proposed solution was approved.
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22

B.N, Rashmi. "Offline Signature Verification and Identification using Dimensionality Reduction." International Journal of Computer Applications 117, no. 20 (May 20, 2015): 4–6. http://dx.doi.org/10.5120/20668-3250.

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23

Umeda, Michio, Takeo Miyoshi, and Kiichi Misaki. "Writer Identification and Verification Using Autoassociative Neural Networks." IEEJ Transactions on Electronics, Information and Systems 122, no. 11 (2002): 1869–75. http://dx.doi.org/10.1541/ieejeiss1987.122.11_1869.

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24

Al-Juburi, Ban Jaber Adnan, Professor Hind Rustum Mohammed, and Assad Noori Hashim Al-Shareefi. "Iris Recognitions Identification and Verification using Hybrid Techniques." Research Journal of Applied Sciences, Engineering and Technology 14, no. 12 (December 15, 2017): 473–82. http://dx.doi.org/10.19026/rjaset.14.5150.

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25

Mandemakers, Wim, Roy Masius, Michelle Minneboo, Dick Dekkers, Jeroen Demmers, Max Kros, and Vincenzo Bonifati. "Identification and verification of novel FBXO7 interacting proteins." Parkinsonism & Related Disorders 22 (January 2016): e171. http://dx.doi.org/10.1016/j.parkreldis.2015.10.419.

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26

Kalimoldayev, M. N., O. Zh Mamyrbayev, A. S. Kydyrbekova, and N. O. Mekebayev. "Voice verification and identification using i-vector representation." International Journal of Mathematics and Physics 10, no. 1 (2019): 66–74. http://dx.doi.org/10.26577/ijmph-2019-i1-9.

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27

Durani, Aqib Mehmood, Mumtaz Ali, Rameez Ahmad, Syed Irfan, and Habib ur Rehman. "Identification and Verification of Vehicle using RFID Technique." VAWKUM Transactions on Computer Sciences 10, no. 2 (July 28, 2016): 1. http://dx.doi.org/10.21015/vtcs.v10i2.390.

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28

Matthews, H. B., Stephen M. Miller, and James B. Rawlings. "Model identification for crystallization: Theory and experimental verification." Powder Technology 88, no. 3 (September 1996): 227–35. http://dx.doi.org/10.1016/s0032-5910(96)03125-7.

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29

Strojnik, Marija. "Experimental verification of a star field identification algorithm." Optical Engineering 33, no. 4 (April 1, 1994): 1120. http://dx.doi.org/10.1117/12.165152.

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30

Lee, D., and M. Yannakakis. "Testing finite-state machines: state identification and verification." IEEE Transactions on Computers 43, no. 3 (March 1994): 306–20. http://dx.doi.org/10.1109/12.272431.

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31

Honc, Daniel, and Eleonora Riva Sanseverino. "Magnetic Levitation – Modelling, Identification and Open Loop Verification." TRANSACTIONS ON ELECTRICAL ENGINEERING 8, no. 1 (March 30, 2020): 13–16. http://dx.doi.org/10.14311/tee.2019.1.013.

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<p>The paper describes a procedure using the first principle modelling and experimental identification of the Magnetic Levitation Model CE 152. It is a modified version of the paper [1]. The difference is that the identification and verification is done in open loop and constraints logic is added in the current paper. The author optimized and simplified dynamic model to a minimum to what is needed to characterize given system for the simulation and control design purposes. Only few open-loop experiments are needed to estimate the unknown parameters. Model quality is verified in open loop where the real and simulated data are compared. The model can serve as a simulation model for some standard control algorithms or as a process model for advanced control method design.</p>
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32

Rusli, Leonard, and Anthony Luscher. "Fastener identification and assembly verification via IR tracking." Assembly Automation 32, no. 3 (July 27, 2012): 262–75. http://dx.doi.org/10.1108/01445151211244429.

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33

Fang, Yong, Yue Yang, and Cheng Huang. "EmailDetective: An Email Authorship Identification And Verification Model." Computer Journal 63, no. 11 (July 13, 2020): 1775–87. http://dx.doi.org/10.1093/comjnl/bxaa059.

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Abstract Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author’s writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model’s accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors.
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34

Adán, Miguel, Antonio Adán, Andrés S. Vázquez, and Roberto Torres. "Biometric verification/identification based on hands natural layout." Image and Vision Computing 26, no. 4 (April 2008): 451–65. http://dx.doi.org/10.1016/j.imavis.2007.08.010.

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35

Bertolini, D., L. S. Oliveira, E. Justino, and R. Sabourin. "Texture-based descriptors for writer identification and verification." Expert Systems with Applications 40, no. 6 (May 2013): 2069–80. http://dx.doi.org/10.1016/j.eswa.2012.10.016.

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36

Gurkan, Necdet, and Jordan W. Suchow. "Causal inference in face identification, matching, and verification." Journal of Vision 21, no. 9 (September 27, 2021): 2754. http://dx.doi.org/10.1167/jov.21.9.2754.

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37

Zhang, Shun, Yantao He, Jiang Wei, Shaohui Mei, Shuai Wan, and Ke Chen. "Person Re-Identification With Joint Verification and Identification of Identity-Attribute Labels." IEEE Access 7 (2019): 126116–26. http://dx.doi.org/10.1109/access.2019.2939071.

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38

Ubul, Kurban, Rayima Ablikim, Nurbiya Yadikar, and Mavjuda Zunun. "Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey." Applied Mechanics and Materials 519-520 (February 2014): 606–10. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.606.

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Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid processing of documents. Researchers have been proposed various approaches for handwritten signature recognition and verification in the past years. This paper presents a survey for non-western handwritten signature based offline signature verification and identification. In this area, the accuracy rates obtained so far from the available systems is not sufficiently high, and more researches on off-line signature verification as well as off-line signature identification are required.
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39

Manda, Bappaditya, Xudong Jiang, and Alex Kot. "Face Verification Using Modeled Eigenspectrum." Open Artificial Intelligence Journal 2, no. 1 (June 9, 2008): 35–45. http://dx.doi.org/10.2174/1874061800802010035.

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Face verification is different from face identification task. Some traditional subspace methods that work well in face identification may suffer from severe over-fitting problem when applied for the verification task. Conventional discriminative methods such as linear discriminant analysis (LDA) and its variants are highly sensitive to the training data, which hinders them from achieving high verification accuracy. This work proposes an eigenspectrum model that alleviates the over-fitting problems by replacing the unreliable small and zero eigenvalues with the model values. It also enables the discriminant evaluation in the whole space to extract the low dimensional features effectively. The proposed approach is evaluated and compared with 8 popular subspace based methods for a face verification task. Experimental results on three face databases show that the proposed method consistently outperforms others.
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40

SHU, WEI, GANG RONG, ZHAOQI BIAN, and DAVID ZHANG. "AUTOMATIC PALMPRINT VERIFICATION." International Journal of Image and Graphics 01, no. 01 (January 2001): 135–51. http://dx.doi.org/10.1142/s0219467801000104.

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Automic palmprint verification is an important complement of biometric authentication. As the first attempt of personal identification by palmprint, this paper explores different methods for three main processing stages in palmprint verification including datum point registration, line feature extraction and palmprint classification. The datum points of palmprint which have the remarkable advantage of invariable location are defined and their determination method using the directional projection algorithm is improved. Then, line feature extraction and line matching method is described to detect whether a couple of palmprints are from the same palm. In addition, palmprint classification method based on the orientation property of the ridges is discussed to distinguish six typical cases. Various palmprint images have been tested to illustrate the effectiveness of the proposed methods.
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41

ZACHARIAS, I. "Verification of seiching processes in a large and deep lake (Trichonis, Greece)." Mediterranean Marine Science 1, no. 1 (June 1, 2000): 79. http://dx.doi.org/10.12681/mms.279.

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A computational analysis of the periods and structure of surface seiches of Lake Trichonis in Greece and its experimental verification from three simultaneous water gauge recordings, mounted along the shores in Myrtia, Panetolio and Trichonio is given. The first five theoretical modes are calculated with a finite difference code of tidal equations, which yield the eigenperiodes, co-range and co-tidal lines that are graphically displayed and discussed in detail.Experimental verifications are from recordings taken during spring. Visual observations of the record permit identification of the five lowest order modes, including inter station phase shift. Power spectral analysis of two time series and interstation phase difference and coherence spectra allow the identification of the same five modes. Agreement between the theoretically predicted and the experimentally determined periods was excellent for most of the calculated modes.
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42

Klinger, Volkhard. "SMoBAICS." International Journal of Privacy and Health Information Management 5, no. 2 (July 2017): 34–57. http://dx.doi.org/10.4018/ijphim.2017070103.

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Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.
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43

Perry, Anthony R., William N. Dember, Joel S. Warm, and Joel G. Sacks. "Letter identification in normal and dyslexic readers: A verification." Bulletin of the Psychonomic Society 27, no. 5 (May 1989): 445–48. http://dx.doi.org/10.3758/bf03334651.

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44

Saeed, Khalid. "Toeplitz-Based Voice Verification for Human Identification - Extensive Review." Recent Patents on Electrical Engineeringe 1, no. 3 (November 1, 2008): 238–43. http://dx.doi.org/10.2174/1874476110801030238.

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45

Upadhyay, Shrikant, and Sudhir Kumar Sharma. "Robust Speaker Identification and Verification in Adverse Acoustic Condition." INROADS- An International Journal of Jaipur National University 8, no. 1and2 (2019): 14. http://dx.doi.org/10.5958/2277-4912.2019.00002.x.

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46

Chesnokova, Elena V. "Analysis of Expert Errors in Vehicle Identification Number Verification." Theory and Practice of Forensic Science 12, no. 2 (June 30, 2017): 75–81. http://dx.doi.org/10.30764/1819-2785-2017-12-2-75-81.

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47

Tovkai, O. A., V. O. Palamarchuk, P. O. Lishchynskyi, and O. V. Mazur. "Parathyroid glands verification using the auto-fluorescence identification tool." Clinical Endocrinology and Endocrine Surgery, no. 1 (March 27, 2020): 7–13. http://dx.doi.org/10.30978/cees-2020-1-7.

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48

Gumusbas, Dilara, and Tulay Yildirim. "Offline Signature Identification and Verification Based on Capsule Representations." Cybernetics and Information Technologies 20, no. 5 (December 1, 2020): 60–67. http://dx.doi.org/10.2478/cait-2020-0040.

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AbstractOffline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.
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49

Balasubramanian, M., S. Palanivel, and V. Ramalingam. "Fovea intensity comparison code for person identification and verification." Engineering Applications of Artificial Intelligence 23, no. 8 (December 2010): 1277–90. http://dx.doi.org/10.1016/j.engappai.2010.07.006.

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50

Leng, Biao, Yu Liu, Kai Yu, Songting Xu, Ziqing Yuan, and Jingyan Qin. "Cascade shallow CNN structure for face verification and identification." Neurocomputing 215 (November 2016): 232–40. http://dx.doi.org/10.1016/j.neucom.2015.08.134.

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