Konferans Bildirisi
BibTex RIS Kaynak Göster

Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation

Yıl 2023, Cilt: 2 Sayı: 2, 247 - 252, 27.12.2023

Zahraa Talal , Ahmed M. Alkababji

Öz

The study proposes a multimodal biometric design that combines face and palm print recognition modules. To extract the features from the face data set, we proposed a novel 2-D circular wavelet filter that depends on HAAR filters and used the contourlet transformation in palm print data sets. The multimodal biometric design merges the features extracted from different types of unimodal system UBS by using a fusion level. Our proposed approach wants to decrease the time required to recognize a person depending on 2-D CDWT and enhance the accuracy of recognition by using the 2-D CDWT and contourlet transformations as pre-processing level in our approach, then the CNN model is applied to train and test our approach. Our data set was taken from 110 persons which means 1100 pairs of images in 10 sessions. This approach’s results look good and progressed over other most recent architectures by recording a precision of 99.3%, with a score-level fusion.

Anahtar Kelimeler

face recognition palm print recognition multimodal biometric system 2D circular wavelet filter and contourlet transformation.

Destekleyen Kurum

no supporting institution

Proje Numarası

ICES-225

Kaynakça

  • Agrawal, P., Zabrovskiy, A., Ilangovan, A., Timmerer, C., & Prodan, R. (2021). FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos. Cluster Computing,24(3),1605–1621.doi:https://doi.org/10.1007/s10586-020-03207-x
  • Bai, Y., Haghighat, M., & Abdel-Mottaleb, M. (2018). Kernel Discriminant Correlation Analysis: Feature Level Fusion for Nonlinear Biometric Recognition. P2018 24th International Conference on Pattern Recognition(ICPR),3198–3203. doi:https://doi.org/10.1109/ICPR.2018.8546068
  • Bc, A., & Prakash, H. N. (2022). Image fusion by discrete wavelet transform for multimodal biometric recognition. IAES International Journal of Artificial Intelligence (IJ-AI), 11(1), 229–237. doi:https://doi.org/10.11591/ijai.v11.i1.pp229-237
  • Hardalac, F., Yaşar, H., Akyel, A., & Kutbay, U. (2020). A novel comparative study using multi- Talal Abed, Alkababji Journal of Optimization & Decision Making 2(2), 247-252, 2023 252 resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification. Multimedia Tools and Applications, 79(31–32), 22929–22963. doi:https://doi.org/10.1007/s11042-020-09005-2
  • Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors (Switzerland), 20(2). https://doi.org/10.3390/s20020342
  • Leghari, M., Memon, S., & Chandio, A. A. (2018). Feature-Level Fusion of Fingerprint and Online Signature for Multimodal Biometrics. 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), 2–5. doi:https://doi.org/10.1109/ICOMET.2018.8346358
  • Mansoura, L., Noureddine, A., Assas, O., & Yassine, A. (2019). Multimodal Face and Iris Recognition with Adaptive Score Normalization using Several Comparative Methods. Indian Journal of Science and Technology, 12(7),1–8. doi:https://doi.org/10.17485/ijst/2019/v12i7/140755
  • Nada Alay, H. H. A.-B. (2020). Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits. Sensors, 20(19), 5523–5530. doi:https://doi.org/10.3390/s20195523.
  • Oloyede, M. O., & Hancke, G. P. (2016). Unimodal and Multimodal Biometric Sensing Systems: A Review. IEEE Access, 4, 7532–7555. doi:https://doi.org/10.1109/ACCESS.2016.2614720
  • Regouid, M., Touahria, M., Benouis, M., & Costen, N. (2019). Multimodal biometric system for ECG , ear and iris recognition based on local descriptors. Multimed Tools Appl, 78, 22509–22535. doi:https://doi.org/10.1007/s11042-019-7467-x
  • s, K. R. (2023). A Deep Learning Technique for Bi-Fold Grading of an Eye Disorder DR-Diabetic Retinopathy. Data Analytics and Artificial Intelligence, 3(2), 113–115. doi:https://doi.org/10.1007/978-981-19-0151-5_32
  • Singhal, P., & Kumar, A. (2022). FACE RECOGNITION USING PCA AND WAVELET TRANSFORM. Advances and Application in Mathematical Sciences, 21(5), 2795–2802.
  • Sujatha, E., & Chilambuchelvan, A. (2017). Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT. Wireless Personal Communications, 99(1), 23–34. doi: https://doi.org/10.1007/s11277-017-5034-1
  • T., V. (2021). Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature. Journal of Innovative Image Processing, 3(2), 131–143. doi:https://doi.org/10.36548/jiip.2021.2.005
  • Tabassum, F., Imdadul Islam, M., Tasin Khan, R., & Amin, M. R. (2022). Human face recognition with combination of DWT and machine learning. Journal of King Saud University - Computer and Information Sciences, 34(3), 546–556. doi:https://doi.org/10.1016/j.jksuci.2020.02.002
  • Tarawneh, A. S., Chetverikov, D., & Hassanat, A. B. (2018). Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images. Ninth Hungarian Conference on Computer Graphics and Geometry, 1804–1810. doi:https://doi.org/10.48550/arXiv.1804.04602
  • Wang, Y., Peng, L., & Zhe, F. (2018). Face recognition using slow feature analysis and contourlet transform. AIP Conference Proceedings,1955, 040155–040161. doi:https://doi.org/10.1063/1.5033819

Yıl 2023, Cilt: 2 Sayı: 2, 247 - 252, 27.12.2023

Zahraa Talal , Ahmed M. Alkababji

Öz

Proje Numarası

ICES-225

Kaynakça

  • Agrawal, P., Zabrovskiy, A., Ilangovan, A., Timmerer, C., & Prodan, R. (2021). FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos. Cluster Computing,24(3),1605–1621.doi:https://doi.org/10.1007/s10586-020-03207-x
  • Bai, Y., Haghighat, M., & Abdel-Mottaleb, M. (2018). Kernel Discriminant Correlation Analysis: Feature Level Fusion for Nonlinear Biometric Recognition. P2018 24th International Conference on Pattern Recognition(ICPR),3198–3203. doi:https://doi.org/10.1109/ICPR.2018.8546068
  • Bc, A., & Prakash, H. N. (2022). Image fusion by discrete wavelet transform for multimodal biometric recognition. IAES International Journal of Artificial Intelligence (IJ-AI), 11(1), 229–237. doi:https://doi.org/10.11591/ijai.v11.i1.pp229-237
  • Hardalac, F., Yaşar, H., Akyel, A., & Kutbay, U. (2020). A novel comparative study using multi- Talal Abed, Alkababji Journal of Optimization & Decision Making 2(2), 247-252, 2023 252 resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification. Multimedia Tools and Applications, 79(31–32), 22929–22963. doi:https://doi.org/10.1007/s11042-020-09005-2
  • Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors (Switzerland), 20(2). https://doi.org/10.3390/s20020342
  • Leghari, M., Memon, S., & Chandio, A. A. (2018). Feature-Level Fusion of Fingerprint and Online Signature for Multimodal Biometrics. 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), 2–5. doi:https://doi.org/10.1109/ICOMET.2018.8346358
  • Mansoura, L., Noureddine, A., Assas, O., & Yassine, A. (2019). Multimodal Face and Iris Recognition with Adaptive Score Normalization using Several Comparative Methods. Indian Journal of Science and Technology, 12(7),1–8. doi:https://doi.org/10.17485/ijst/2019/v12i7/140755
  • Nada Alay, H. H. A.-B. (2020). Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits. Sensors, 20(19), 5523–5530. doi:https://doi.org/10.3390/s20195523.
  • Oloyede, M. O., & Hancke, G. P. (2016). Unimodal and Multimodal Biometric Sensing Systems: A Review. IEEE Access, 4, 7532–7555. doi:https://doi.org/10.1109/ACCESS.2016.2614720
  • Regouid, M., Touahria, M., Benouis, M., & Costen, N. (2019). Multimodal biometric system for ECG , ear and iris recognition based on local descriptors. Multimed Tools Appl, 78, 22509–22535. doi:https://doi.org/10.1007/s11042-019-7467-x
  • s, K. R. (2023). A Deep Learning Technique for Bi-Fold Grading of an Eye Disorder DR-Diabetic Retinopathy. Data Analytics and Artificial Intelligence, 3(2), 113–115. doi:https://doi.org/10.1007/978-981-19-0151-5_32
  • Singhal, P., & Kumar, A. (2022). FACE RECOGNITION USING PCA AND WAVELET TRANSFORM. Advances and Application in Mathematical Sciences, 21(5), 2795–2802.
  • Sujatha, E., & Chilambuchelvan, A. (2017). Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT. Wireless Personal Communications, 99(1), 23–34. doi: https://doi.org/10.1007/s11277-017-5034-1
  • T., V. (2021). Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature. Journal of Innovative Image Processing, 3(2), 131–143. doi:https://doi.org/10.36548/jiip.2021.2.005
  • Tabassum, F., Imdadul Islam, M., Tasin Khan, R., & Amin, M. R. (2022). Human face recognition with combination of DWT and machine learning. Journal of King Saud University - Computer and Information Sciences, 34(3), 546–556. doi:https://doi.org/10.1016/j.jksuci.2020.02.002
  • Tarawneh, A. S., Chetverikov, D., & Hassanat, A. B. (2018). Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images. Ninth Hungarian Conference on Computer Graphics and Geometry, 1804–1810. doi:https://doi.org/10.48550/arXiv.1804.04602
  • Wang, Y., Peng, L., & Zhe, F. (2018). Face recognition using slow feature analysis and contourlet transform. AIP Conference Proceedings,1955, 040155–040161. doi:https://doi.org/10.1063/1.5033819
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik
Bölüm Araştırma Makalesi
Yazarlar

Zahraa Talal

Ahmed M. Alkababji

Proje Numarası ICES-225
Erken Görünüm Tarihi 27 Aralık 2023
Yayımlanma Tarihi 27 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 2 Sayı: 2

Kaynak Göster

APA Talal, Z., & M. Alkababji, A. (2023). Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation. Journal of Optimization and Decision Making, 2(2), 247-252.
AMA Talal Z, M. Alkababji A. Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation. Journal of Optimization and Decision Making. Aralık 2023;2(2):247-252.
Chicago Talal, Zahraa, ve Ahmed M. Alkababji. “Face-Palm Print Recognition System Based on 2d Circular Wavelet Filter and Contourlet Transformation”. Journal of Optimization and Decision Making 2, sy. 2 (Aralık 2023): 247-52.
EndNote Talal Z, M. Alkababji A (01 Aralık 2023) Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation. Journal of Optimization and Decision Making 2 2 247–252.
IEEE Z. Talal ve A. M. Alkababji, “Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation”, Journal of Optimization and Decision Making, c. 2, sy. 2, ss. 247–252, 2023.
ISNAD Talal, Zahraa - M. Alkababji, Ahmed. “Face-Palm Print Recognition System Based on 2d Circular Wavelet Filter and Contourlet Transformation”. Journal of Optimization and Decision Making 2/2 (Aralık 2023), 247-252.
JAMA Talal Z, M. Alkababji A. Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation. Journal of Optimization and Decision Making. 2023;2:247–252.
MLA Talal, Zahraa ve Ahmed M. Alkababji. “Face-Palm Print Recognition System Based on 2d Circular Wavelet Filter and Contourlet Transformation”. Journal of Optimization and Decision Making, c. 2, sy. 2, 2023, ss. 247-52.
Vancouver Talal Z, M. Alkababji A. Face-palm print recognition system based on 2d circular wavelet Filter and contourlet transformation. Journal of Optimization and Decision Making. 2023;2(2):247-52.
 Kapak Resmi İndir