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  1. 1
    Fingerprint verification using clonal selection algorithm / Farah Syadiyah Shamsudin

    Fingerprint verification using clonal selection algorithm / Farah Syadiyah Shamsudin by Shamsudin, Farah Syadiyah

    Published 2017
    “…Therefore, the aim for this project is to develop a new approach in the fingerprint verification system by applying Clonal Selection Algorithm (CSA) that is known to be good in pattern matching and optimization of problems. …”
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  2. 2
    Finger vein verification system using repeated line tracking and dimensionality reduction using PCA algorithms with SURF matching

    Finger vein verification system using repeated line tracking and dimensionality reduction using PCA algorithms with SURF matching by Ei Wei., Ting, M. Z., Ibrahim, D.J., Mulvaney

    Published 2017
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  3. 3
    Stroke-to-stroke matching in on-line signature verification

    Stroke-to-stroke matching in on-line signature verification by Ahmad Jaini, Azhar

    Published 2010
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  4. 4
    PERSON IDENTIFICATION BASED ON MULTIMODAL BIOMETRIC RECOGNITION

    PERSON IDENTIFICATION BASED ON MULTIMODAL BIOMETRIC RECOGNITION by ALEX, NG HO LIAN

    Published 2020
    “…In the process of fingerprint recognition, the ORB algorithm is recommended to use in feature matching. …”
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  5. 5
    Face verification : an introduction

    Face verification : an introduction by Hameed, Shihab A., Badurik, Waleed A.

    Published 2011
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  6. 6
    An enhanced fingerprint template protection scheme

    An enhanced fingerprint template protection scheme by Siswanto, Apri

    Published 2022
    “…Furthermore, an improved chaos-based encryption algorithm was proposed for encrypting FT. The MATLAB simulation with Fingerprint Verification Competition (FVC) 2002 database was used to measure the encryption results, secret key spaces, key sensitivity, histogram, correlation, differential, entropy information, matching/recognition analysis, and revocability. …”
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  7. 7
    Signature verification system using support vector machine

    Signature verification system using support vector machine by Fauziyah, Salehuddin, Azlina , Othman

    Published 2009
    “…The common verification algorithm is one of the Global Feature Vector Machine called Support Vector Machine (SVM). …”
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  8. 8
    Partial fingerprint recognition using support vector machine

    Partial fingerprint recognition using support vector machine by Vijayaprasad, Perumal, Sulaiman, Md. Nasir, Mustapha, Norwati, O. K. Rahmat, Rahmita Wirza

    Published 2010
    “…Global minutiae-based matching algorithm is used to record the matching pairs and their feature vectors are used to generate a model file which is used for classification. …”
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  9. 9
    Person Verification Based on Multimodal Biometric Recognition

    Person Verification Based on Multimodal Biometric Recognition by Annie, Joseph, Ng, Alex Ho Lian, Kuryati, Kipli, Kho, Lee Chin, Dayang Azra, Awang Mat, Sia, Charlie Chin Voon, Chua, David Sing Ngie, Ngu, Sze Song

    Published 2022
    “…The verification process is matched if the fusion score is greater than the pre-set threshold t. …”
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  10. 10
    Development of efficient iris identification algorithm using wavelet packets for smartphone application

    Development of efficient iris identification algorithm using wavelet packets for smartphone application by Gunawan, Teddy Surya, Solihin, Nurul Shaieda, Morshidi, Malik Arman, Kartiwi, Mira

    Published 2017
    “…Nowadays, iris recognition is widely used for personal identification and verification based on biometrical technology, especially in the smartphone arena. …”
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  11. 11
    Non-fiducial based electrocardiogram biometrics with kernel methods

    Non-fiducial based electrocardiogram biometrics with kernel methods by Hejazi, Maryamsadat

    Published 2017
    “…At classification level, Gaussian multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach is proposed to evaluate verification performance rates of the feature extraction algorithms. …”
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  12. 12
    Speaker verification based on speech signal

    Speaker verification based on speech signal by Shariffah Fauziah, Jap, Chekima, Ali, Mazlina, Mamat, Wan Mahani, Abdullah

    Published 2009
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  13. 13
    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition

    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

    Published 2002
    “…In addition to the Arabic speech data that used in the original experiments, for both speaker dependant and speaker independent tests, more verification experiments were conducted using the TI20 speech data. …”
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  14. 14
    Moving objects detection from UAV captured videos using trajectories of matched regional adjacency graphs

    Moving objects detection from UAV captured videos using trajectories of matched regional adjacency graphs by Harandi, Bahareh Kalantar Ghorashi

    Published 2017
    “…In the past, MOD was mainly tackled using image registration, which discovers correspondences between consecutive frames using pair-wise grayscale spatial visual appearance matching under rigid and affine transformations. …”
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  15. 15
    Fingerprint recognition using neural networks / Kennie Yeoh Eng Hoe

    Fingerprint recognition using neural networks / Kennie Yeoh Eng Hoe by Kennie Yeoh , Eng Hoe

    Published 2001
    “…Traditional methods of fingerprint verification uses either complicated feature detection algorithms that are not specific to each fingerprint, or compare two fingerprint images directly using image processing toots. …”
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  16. 16
    Multispectral palm vein image fusion for contactless palm vein verification system

    Multispectral palm vein image fusion for contactless palm vein verification system by Soh, Shi Chuan

    Published 2018
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  17. 17
    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System by Zainal Abidin, Zaheera, -, M.Manaf, Shibghatullah, Abdul Samad, -, S.H.A.Mohd Yunos, -, S.Anawar, -, Z.Ayop

    Published 2012
    “…Then the image captured is segmented and normalized for encoding process. The matching technique, Hamming Distance, is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage. …”
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  18. 18
    A biometric fingerprint recognition system utilizing the Scale Invariant Feature Transform (SIFT) algorithm for border crossing

    A biometric fingerprint recognition system utilizing the Scale Invariant Feature Transform (SIFT) algorithm for border crossing by Hamzah N.A., Jaaz Z.A., Al-Bakri N.F., Tawfeq J.F., Radhi A.D.

    Published 2024
    “…The SIFT algorithm has some drawbacks and is currently widely used because of its overall efficiency, although the efficiency of a single collection is slow. …”
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  19. 19
    Development of biosignals-based multimodal biometric system

    Development of biosignals-based multimodal biometric system by Osamah Sadeq Alhamdani

    Published 2014
    “…Heart Sound Verification (HSV) provides an average equal error rate (EER) of 13.8% while the average EER for the Speaker Verification model (SV) is 2.1 %. …”
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  20. 20
    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System

    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System by Zainal Abidin, Zaheera, Anawar, Syarulnaziah, Ayop, Zakiah, Manaf, Mazani, Shibghatullah, A.S., Mohd Yunos, S.H.A.

    Published 2012
    “…Iris segmentation is foremost part of iris recognition system.There are four steps in iris recognition: segmentation,normalization,encoding and matching.Here, iris segmentation has been implemented using Hough Transform and IntegroDifferential Operator techniques.The performance of iris recognition system depends on segmentation and normalization technique.Iris recognition systems capture an image from individual eye.Then the image captured is segmented and normalized for encoding process.The matching technique,Hamming Distance,is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage.These processes produce values of average circle pupil,average circle iris,error rate and edge points.The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR).Hough Transform algorithm,provide better performance,at the expense of higher computational complexity.It is used to evolve a contour that can fit to a non-circular iris boundary.However,edge information is required to control the evolution and stopping the contour.The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information.The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator.…”
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