The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Handbook of Biometrics
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The expression swap and attribute manipulation, although only tampers with the soft biometrics (Jain et al. 2007) (pertains to facial attributes) of the subject, may also pose risks to certain applicable scenarios where people can easily verify the identity of the subject, but not what she/he says, such as in political elections”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Jain et al. 2007
In the citing paper
“The identity swap, altering the hard biometrics (Jain et al. 2007) (pertains to identity information) of a subject, poses risks to a wide range of safety-critical scenarios since the identity information is tampered.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Jain et al. 2007
Citations
Cites
Jain, A. K., Ross, A. A., & Nandakumar, K. (2011) · Springer eBooks · doi:10.1007/978-0-387-77326-1 · Cited by 992
Self-citationZhang, D. D., & Kanhangad, V. (2011) · Springer eBooks · doi:10.1007/978-1-4419-5906-5_878 · Cited by 10
Cited by
Sorted by citations
A survey on biometric cryptosystems and cancelable biometrics
Rathgeb, C., & Uhl, A. (2011) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2011-3 · Cited by 643
Template Matching Techniques in Computer Vision
Brunelli, R. (2009) · Wiley eBooks · doi:10.1002/9780470744055 · Cited by 892
Javier Galbally, A., Marcel, S., & Fiérrez, J. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2292332 · Cited by 611
Gait recognition using wifi signals
Wang, W., Liu, A. X., & Shahzad, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971670 · Cited by 582
Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model
Tan, X., Li, Y., Liu, J., & Jiang, L. H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_37 · Cited by 531
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics
Dantcheva, A., Elia, P., & Ross, A. A. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2480381 · Cited by 431
Self-citationOnline finger-knuckle-print verification for personal authentication
Lei Zhang, A., Lei Zhang, A., Zhang, D. D., & Zhu, H. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.01.020 · Cited by 346
1Deep Representations for Iris, Face, and Fingerprint Spoofing DetectionPreprint
Menottiy, D., Chiachia, G., Pinto, A. D. S., Member, S., Schwartz, W. R., Pedrini, H., Falcão, R. X., & Rocha, A. D. R. (2016) · Cited by 352
Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing
Jung, Y.-H., Hong, S. K., Wang, H. S., Han, J. H., Pham, T. X., Park, H., Kim, J., Kang, S., Yoo, C. D., & Lee, K. J. (2019) · Advanced Materials · doi:10.1002/adma.201904020 · Cited by 344
Counter-measures to photo attacks in face recognition: A public database and a baseline
Anjos, A., & Marcel, S. (2011) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb.2011.6117503 · Cited by 322
LBP − TOP Based Countermeasure against Face Spoofing Attacks
Pereira, T. D. F., Anjos, A., Martino, J. M. D., & Marcel, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37410-4_11 · Cited by 319
Personal Authentication Using Hand Vein Triangulation and Knuckle Shape
Kumar, A., & Prathyusha, K. V. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2023153 · Cited by 307
Zhang, L., Li, L., Yang, A., Shen, Y., & Yang, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.016 · Cited by 294
Can face anti-spoofing countermeasures work in a real world scenario?
Pereira, T. D. F., Anjos, A., Martino, J. M. D., & Marcel, S. (2013) · International Conference on Biometrics · doi:10.1109/icb.2013.6612981 · Cited by 272
Face liveness detection using dynamic texture
Pereira, T. D. F., Komulainen, J., Anjos, A., Martino, J. M. D., Hadid, A., Pietikäinen, M., & Marcel, S. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-2 · Cited by 263
Security and Accuracy of Fingerprint-Based Biometrics: A Review
Yang, W., Wang, S., Hu, J., Zheng, G., & Valli, C. (2019) · Symmetry · doi:10.3390/sym11020141 · Cited by 273
A Comparative Study of Palmprint Recognition Algorithms
Zhang, D. D., Zuo, W., & Feng, Y. (2012) · ACM Computing Surveys · doi:10.1145/2071389.2071391 · Cited by 247
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
Ortega-García, J., Fiérrez, J., Alonso‐Fernandez, F., Javier Galbally, A., Freire, M. R., González-Rodríguez, J., Mateo, C. G., Alba‐Castro, J. L., González-Agulla, E., Otero-Muras, E., et al. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.76 · Cited by 241
Abaza, A., Ross, A., Hebert, C., Harrison, M. A. F., & Nixon, M. S. (2013) · ACM Computing Surveys · doi:10.1145/2431211.2431221 · Cited by 178
Self-citationPrivacy in mobile technology for personal healthcare
Avancha, S., Baxi, A., & Kotz, D. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379779 · Cited by 229
Ensemble of local and global information for finger–knuckle-print recognition
Zhang, L., Lei Zhang, A., Zhang, D. D., & Zhu, H. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.06.007 · Cited by 205
Soft Biometric Traits for Continuous User Authentication
Niinuma, K., Park, U., & Jain, A. K. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2075927 · Cited by 229
Self-citationGREYC keystroke: A benchmark for keystroke dynamics biometric systems
Giot, R., El-Abed, M., & Rosenberger, C. (2009) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2009.5339051 · Cited by 201
Using machine learning to detect events in eye-tracking data
Zemblys, R., Niehorster, D. C., Komogortsev, O. V., & Holmqvist, K. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0860-3 · Cited by 200
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
Afifi, M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7424-8 · Cited by 194
Wang, C., Muhammad, J., Wang, Y., He, Z., & Sun, Z. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2980791 · Cited by 187
Cancelable Biometrics: a comprehensive survey
Manisha, M., & Kumar, N. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09767-8 · Cited by 184
Al‐Waisy, A. S., Al-Fahdawi, S., Mohammed, M. A., Abdulkareem, K. H., Mostafa, S. A., Maashi, M. S., Arif, M., & García-Zapirain, B. (2020) · Soft Computing · doi:10.1007/s00500-020-05424-3 · Cited by 187
RetractedBiometric Recognition: An Overview
Jain, A. K., & Kumar, A. (2012) · The International Library of Ethics, Law and Technology · doi:10.1007/978-94-007-3892-8_3 · Cited by 176
Self-citationA survey of biometric technology based on hand shape
Duta, N. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.02.007 · Cited by 165
Biometric identification via eye movement scanpaths in reading
Holland, C. D., & Komogortsev, O. V. (2011) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb.2011.6117536 · Cited by 164
Visual Cryptography for Biometric Privacy
Ross, A. A., & Othman, A. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2097252 · Cited by 163
Self-citationA hybrid biometric cryptosystem for securing fingerprint minutiae templates
Nagar, A., Nandakumar, K., & Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.07.003 · Cited by 155
Self-citationMultifeature-Based High-Resolution Palmprint Recognition
Dai, J., & Zhou, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.164 · Cited by 154
Theoretical and Empirical Considerations of Guests’ Perceptions of Biometric Systems in Hotels
Morosan, C. V. (2010) · Journal of Hospitality & Tourism Research · doi:10.1177/1096348010380601 · Cited by 150
New trends on digitisation of complex engineering drawings
Moreno‐García, C. F., Elyan, E., & Jayne, C. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3583-1 · Cited by 164
Motion‐based counter‐measures to photo attacks in face recognition
Anjos, A., Chakka, M. M., & Marcel, S. (2013) · IET Biometrics · doi:10.1049/iet-bmt.2012.0071 · Cited by 149
On the need for different security methods on mobile phones
Ben‐Asher, N., Kirschnick, N., Sieger, H., Meyer, J., Ben-Oved, A., & Möller, S. (2011) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2037373.2037442 · Cited by 133
Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks
Pinto, A. D. S., Schwartz, W. R., Pedrini, H., & Rocha, A. D. R. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2395139 · Cited by 132
ChatGPT: ethical concerns and challenges in academics and research
Guleria, A., Krishan, K., Sharma, V., & Kanchan, T. (2023) · The Journal of Infection in Developing Countries · doi:10.3855/jidc.18738 · Cited by 170
A novel hierarchical approach for multispectral palmprint recognition
Hong, D., Liu, W., Su, J. D., Pan, Z., & Wang, G. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.013 · Cited by 125
Gait-Based Person Recognition Using Arbitrary View Transformation Model
Muramatsu, D., Shiraishi, A., Makihara, Y., Uddin, M. Z., & Yagi, Y. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2371335 · Cited by 121
Super-resolution for biometrics: A comprehensive survey
Nguyen, K., Fookes, C., Sridharan, S., Tistarelli, M., & Nixon, M. S. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.002 · Cited by 125
Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation
Zhang, L., Lei Zhang, A., & Zhang, D. D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03767-2_17 · Cited by 120
Phase congruency induced local features for finger-knuckle-print recognition
Zhang, L., Lei Zhang, A., Zhang, D. D., & Guo, Z. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.017 · Cited by 120
Adaptive fingerprint pore modeling and extraction
Zhao, Q., Zhang, D. D., Lei Zhang, A., & Luo, N. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.016 · Cited by 118
Javier Galbally, A., Díaz, M., Ferrer, M. A., Gomez‐Barrero, M., Morales, A., & Fiérrez, J. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.019 · Cited by 116
RETRACTED ARTICLE: Score level based latent fingerprint enhancement and matching using SIFT feature
Manickam, A., Ezhilmaran, D., Manogaran, G., Kumar, P. M., Varatharajan, R., Hsu, C., & Krishnamoorthi, R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5633-1 · Cited by 115
RetractedOnline Student Authentication and Proctoring System Based on Multimodal Biometrics Technology
Labayen, M., Vea, R., Flórez, J., Aginako, N., & Sierra, B. (2021) · IEEE Access · doi:10.1109/access.2021.3079375 · Cited by 113
Hurley, D. J., Arbab‐Zavar, B., & Nixon, M. S. (2007) · Springer eBooks · doi:10.1007/978-0-387-71041-9_7 · Cited by 111
Viejo, C. G., Fuentes, S., Howell, K. S., Torrico, D. D., & Dunshea, F. R. (2018) · Physiology & Behavior · doi:10.1016/j.physbeh.2018.02.051 · Cited by 109
Keystroke dynamics authentication for mobile phones
Maiorana, E., Campisi, P., González-Carballo, N., & Neri, A. (2011) · ACM Symposium on Applied Computing · doi:10.1145/1982185.1982190 · Cited by 107
View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition
Muramatsu, D., Makihara, Y., & Yagi, Y. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2452577 · Cited by 106
A Survey on Healthcare Data: A Security Perspective
Singh, A. K., Anand, A., Lv, Z., Ko, H., & Mohan, A. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3422816 · Cited by 104
Face Recognition of Cattle: Can it be Done?
Kumar, S., Tiwari, S., & Singh, S. K. (2016) · Proceedings of the National Academy of Sciences India Section A Physical Sciences · doi:10.1007/s40010-016-0264-2 · Cited by 100
On the vulnerability of face verification systems to hill-climbing attacks
Javier Galbally, A., McCool, C., Fiérrez, J., Marcel, S., & Ortega-García, J. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.08.022 · Cited by 100
User authentication using keystroke dynamics for cellular phones
Campisi, P., Maiorana, E., Bosco, M. L., & Neri, A. (2009) · IET Signal Processing · doi:10.1049/iet-spr.2008.0171 · Cited by 99
Protection of Privacy in Biometric Data
Natgunanathan, I., Mehmood, A., Xiang, Y., Beliakov, G., & Yearwood, J. L. (2016) · IEEE Access · doi:10.1109/access.2016.2535120 · Cited by 99
Viejo, C. G., Torrico, D. D., Dunshea, F. R., & Fuentes, S. (2019) · Beverages · doi:10.3390/beverages5040062 · Cited by 99
An overview of touchless 2D fingerprint recognition
Priesnitz, J., Rathgeb, C., Buchmann, N., Busch, C., & Margraf, M. (2021) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-021-00548-4 · Cited by 99
A survey of the effects of aging on biometric identity verification
Lanitis, A. (2010) · International Journal of Biometrics · doi:10.1504/ijbm.2010.030415 · Cited by 75
Minutiae Extraction from Fingerprint Images - a ReviewPreprint
Bansal, R., Sehgal, P., & Bedi, P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1201.1422 · Cited by 99
A hybrid latent space data fusion method for multimodal emotion recognition
Nemati, S., Rohani, R., Basiri, M. E., Abdar, M., Yen, N. Y., & Makarenkov, V. (2019) · IEEE Access · doi:10.1109/access.2019.2955637 · Cited by 98
Analysis of pattern recognition techniques for in-air signature biometrics
Bailador, G., Ávila, C. S., Guerra-Casanova, J., & Sierra, A. D. S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.04.010 · Cited by 97
DeepPrivacy2: Towards Realistic Full-Body Anonymization
Hukkelås, H., & Lindseth, F. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00138 · Cited by 109
On-line signature verification using vertical signature partitioning
Cpałka, K., & Zalasiński, M. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.047 · Cited by 94
A score level fusion method for eye movement biometrics
George, A., & Routray, A. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.11.020 · Cited by 93
Palm vein recognition based on multi-sampling and feature-level fusion
Yan, X., Kang, W., Deng, F., & Wu, Q. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.10.019 · Cited by 92
Security in Next Generation Mobile Payment Systems: A Comprehensive Survey
Ahmed, W., Rasool, A., Javed, A. R., Kumar, N., Gadekallu, T. R., Jalil, Z., & Kryvinska, N. (2021) · IEEE Access · doi:10.1109/access.2021.3105450 · Cited by 92
Complex eye movement pattern biometrics: Analyzing fixations and saccades
Holland, C. D., & Komogortsev, O. V. (2013) · International Conference on Biometrics · doi:10.1109/icb.2013.6612953 · Cited by 91
Secure Iris Recognition Based on Local Intensity Variations
Rathgeb, C., & Uhl, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13775-4_27 · Cited by 89
Robust multimodal biometric system based on optimal score level fusion model
Walia, G. S., Singh, T., Singh, K., & Verma, N. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.036 · Cited by 88
Plamondon, R., O’Reilly, C., Javier Galbally, A., Almaksour, A., & Anquetil, É. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.06.004 · Cited by 86
Video-Based Face Spoofing Detection through Visual Rhythm Analysis
Pinto, A. D. S., Pedrini, H., Schwartz, W. R., & Rocha, A. D. R. (2012) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2012.38 · Cited by 85
An effective biometric cryptosystem combining fingerprints with error correction codes
Li, P., Yang, X., Qiao, H., Cao, K., Liu, E., & Tian, J. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.048 · Cited by 85
Detection of obstructive sleep apnea through ECG signal features
Almazaydeh, L. H., Elleithy, K. M., & Faezipour, M. (2012) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2012.6220730 · Cited by 84
Synthetic on-line signature generation. Part I: Methodology and algorithms
Javier Galbally, A., Réjean Plamondon, B., Fiérrez, J., & Ortega-García, J. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.12.011 · Cited by 83
Ocular biometrics in the visible spectrum: A survey
Rattani, A., & Derakhshani, R. R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.11.019 · Cited by 83
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Rahman, A., Chowdhury, M. E. H., Khandakar, A. A., Serkan Kiranyaz, B., Zaman, K. S., Reaz, M. B. I., Islam, M. T., Ezeddin, M., & Kadir, M. A. (2021) · IEEE Access · doi:10.1109/access.2021.3092840 · Cited by 83
Vein pattern database and benchmark results
Kabaciński, R., & Kowalski, M. (2011) · Electronics Letters · doi:10.1049/el.2011.1441 · Cited by 82
Structured Light Techniques and Applications
Bell, T., Li, B., & Zhang, S. (2016) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w8298 · Cited by 82
Hadid, A. (2014) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.22 · Cited by 80
Nanni, L., Maiorana, E., Lumini, A., & Campisi, P. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.023 · Cited by 79
Multispectral scleral patterns for ocular biometric recognition
Crihalmeanu, S., & Ross, A. A. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.11.006 · Cited by 81
Self-citationA systematic review on keystroke dynamics
Pisani, P. H., & Lorena, A. C. (2013) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-013-0117-7 · Cited by 78
Hand vein biometry based on geometry and appearance methods
Yuksel, A., Akarun, L., & Sankur, B. (2011) · IET Computer Vision · doi:10.1049/iet-cvi.2010.0175 · Cited by 77
Consumer preferences of attributes of mobile payment services in South Korea
Choi, H., Park, J., Kim, J., & Jung, Y. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101397 · Cited by 83
Evaluation of emotional responses to television advertising through neuromarketing
Fernández, A. B., González, M. B., Barquero‐Pérez, Ó., Goya–Esteban, R., & de-la-Morena-Gómez, A. (2017) · Comunicar · doi:10.3916/c52-2017-02 · Cited by 75
Evaluation of direct attacks to fingerprint verification systems
Javier Galbally, A., Fiérrez, J., Alonso‐Fernandez, F., & Martinez‐Diaz, M. (2010) · Telecommunication Systems · doi:10.1007/s11235-010-9316-0 · Cited by 74
Forensic evaluation of the evidence using automatic speaker recognition systems
Ramos, D. (2007) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 74
Iris recognition in non-ideal imaging conditions
Li, P., & Ma, H. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.06.017 · Cited by 73
Poh, N., Kittler, J., & Bourlai, T. (2010) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2010.2041660 · Cited by 72
Group sparse representation based classification for multi-feature multimodal biometrics
Goswami, G., Mittal, P., Majumdar, A., Mayank Vatsa, A., & Singh, R. (2015) · Information Fusion · doi:10.1016/j.inffus.2015.06.007 · Cited by 72
Smartphone User Identity Verification Using Gait Characteristics
Damaševičius, R., Maskeliūnas, R., Venčkauskas, A., & Woźniak, M. (2016) · Symmetry · doi:10.3390/sym8100100 · Cited by 71
Securing Mobile Healthcare Data: A Smart Card Based Cancelable Finger-Vein Bio-Cryptosystem
Yang, W., Wang, S., Hu, J., Zheng, G., Chaudhry, J. A., Adi, E., & Valli, C. (2018) · IEEE Access · doi:10.1109/access.2018.2844182 · Cited by 70
Enhanced Security for ATM Machine with OTP and Facial Recognition Features
Karovaliya, M., Karedia, S., Oza, S., & Kalbande, D. R. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.03.166 · Cited by 70
Ear recognition based on local information fusion
Yuan, L., & Mu, Z. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.09.041 · Cited by 70
Fingerprint Recognition using Image Segmentation
Bana, S., & Kaur, D. (2011) · Cited by 69
Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme
Hoang, T., Choi, D., & Nguyen, T. D. (2015) · International Journal of Information Security · doi:10.1007/s10207-015-0273-1 · Cited by 68
Metadata
- Authors
- Jain, A. K., Ross, A. A., & Flynn, P. J.
- Affiliations
- Michigan State University (US)
- University of Notre Dame (US)
- West Virginia University (US)
- Venue
- Springer eBooks
- Publisher
- Springer US
- Published
- 2008
- OpenAlex
- W1523682477
- Cited by
- 1,406
- References
- 2
- Citations by year
- 2026: 25 · 2025: 39 · 2024: 54 · 2023: 46 · 2022: 50 · 2021: 65 · 2020: 68 · 2019: 78 · 2018: 66 · 2017: 82 · 2016: 97 · 2015: 126 · 2014: 113 · 2013: 107 · 2012: 114
- FWCI
- 24.23
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- biometrics, biometric identification, biometric authentication, fingerprint recognition, face recognition, iris recognition, voice recognition, pattern recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review