A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion
The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric syste...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00016.pdf |
| _version_ | 1797213288321253376 |
|---|---|
| author | Najah Kadhim Ola Hasan Abdulameer Mohammed Mahdi Hadi Al-Mayali Yahya |
| author_facet | Najah Kadhim Ola Hasan Abdulameer Mohammed Mahdi Hadi Al-Mayali Yahya |
| author_sort | Najah Kadhim Ola |
| collection | DOAJ |
| description | The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%. |
| first_indexed | 2024-04-24T10:55:54Z |
| format | Article |
| id | doaj.art-f8970dbb95844fee8038d20cccf3c461 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:54Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-f8970dbb95844fee8038d20cccf3c4612024-04-12T07:36:21ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970001610.1051/bioconf/20249700016bioconf_iscku2024_00016A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level FusionNajah Kadhim Ola0Hasan Abdulameer Mohammed1Mahdi Hadi Al-Mayali Yahya2Department of Computer Science, Faculty of Computer Science & Mathematics, University of KufaDepartment of Computer Science, Faculty of Education for Women, University of KufaDepartemenr of Computer Techniques Engineering, College of Technical Engineering, University of AlkafeelThe increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00016.pdf |
| spellingShingle | Najah Kadhim Ola Hasan Abdulameer Mohammed Mahdi Hadi Al-Mayali Yahya A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion BIO Web of Conferences |
| title | A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion |
| title_full | A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion |
| title_fullStr | A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion |
| title_full_unstemmed | A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion |
| title_short | A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion |
| title_sort | multimodal biometric system for iris and face traits based on hybrid approaches and score level fusion |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00016.pdf |
| work_keys_str_mv | AT najahkadhimola amultimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion AT hasanabdulameermohammed amultimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion AT mahdihadialmayaliyahya amultimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion AT najahkadhimola multimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion AT hasanabdulameermohammed multimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion AT mahdihadialmayaliyahya multimodalbiometricsystemforirisandfacetraitsbasedonhybridapproachesandscorelevelfusion |