Periocular Data Fusion for Age and Gender Classification
In recent years, the study of soft biometrics has gained increasing interest in the security and business sectors. These characteristics provide limited biometric information about the individual; hence, it is possible to increase performance by combining numerous data sources to overcome the accura...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/11/307 |
| _version_ | 1797467748304945152 |
|---|---|
| author | Carmen Bisogni Lucia Cascone Fabio Narducci |
| author_facet | Carmen Bisogni Lucia Cascone Fabio Narducci |
| author_sort | Carmen Bisogni |
| collection | DOAJ |
| description | In recent years, the study of soft biometrics has gained increasing interest in the security and business sectors. These characteristics provide limited biometric information about the individual; hence, it is possible to increase performance by combining numerous data sources to overcome the accuracy limitations of a single trait. In this research, we provide a study on the fusion of periocular features taken from pupils, fixations, and blinks to achieve a demographic classification, i.e., by age and gender. A data fusion approach is implemented for this purpose. To build a trust evaluation of the selected biometric traits, we first employ a concatenation scheme for fusion at the feature level and, at the score level, transformation and classifier-based score fusion approaches (e.g., weighted sum, weighted product, Bayesian rule, etc.). Data fusion enables improved performance and the synthesis of acquired information, as well as its secure storage and protection of the multi-biometric system’s original biometric models. The combination of these soft biometrics characteristics combines flawlessly the need to protect individual privacy and to have a strong discriminatory element. The results are quite encouraging, with an age classification accuracy of 84.45% and a gender classification accuracy of 84.62%, respectively. The results obtained encourage the studies on periocular area to detect soft biometrics to be applied when the lower part of the face is not visible. |
| first_indexed | 2024-03-09T18:57:08Z |
| format | Article |
| id | doaj.art-e04915bcf15e4f208233b6b414be73cb |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T18:57:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-e04915bcf15e4f208233b6b414be73cb2023-11-24T05:21:00ZengMDPI AGJournal of Imaging2313-433X2022-11-0181130710.3390/jimaging8110307Periocular Data Fusion for Age and Gender ClassificationCarmen Bisogni0Lucia Cascone1Fabio Narducci2Department of Computer Science, University of Salerno, I-84084 Fisciano, SA, ItalyDepartment of Computer Science, University of Salerno, I-84084 Fisciano, SA, ItalyDepartment of Computer Science, University of Salerno, I-84084 Fisciano, SA, ItalyIn recent years, the study of soft biometrics has gained increasing interest in the security and business sectors. These characteristics provide limited biometric information about the individual; hence, it is possible to increase performance by combining numerous data sources to overcome the accuracy limitations of a single trait. In this research, we provide a study on the fusion of periocular features taken from pupils, fixations, and blinks to achieve a demographic classification, i.e., by age and gender. A data fusion approach is implemented for this purpose. To build a trust evaluation of the selected biometric traits, we first employ a concatenation scheme for fusion at the feature level and, at the score level, transformation and classifier-based score fusion approaches (e.g., weighted sum, weighted product, Bayesian rule, etc.). Data fusion enables improved performance and the synthesis of acquired information, as well as its secure storage and protection of the multi-biometric system’s original biometric models. The combination of these soft biometrics characteristics combines flawlessly the need to protect individual privacy and to have a strong discriminatory element. The results are quite encouraging, with an age classification accuracy of 84.45% and a gender classification accuracy of 84.62%, respectively. The results obtained encourage the studies on periocular area to detect soft biometrics to be applied when the lower part of the face is not visible.https://www.mdpi.com/2313-433X/8/11/307periocular featuresmultimodal fusionmachine learningfusion strategiesprivacy |
| spellingShingle | Carmen Bisogni Lucia Cascone Fabio Narducci Periocular Data Fusion for Age and Gender Classification Journal of Imaging periocular features multimodal fusion machine learning fusion strategies privacy |
| title | Periocular Data Fusion for Age and Gender Classification |
| title_full | Periocular Data Fusion for Age and Gender Classification |
| title_fullStr | Periocular Data Fusion for Age and Gender Classification |
| title_full_unstemmed | Periocular Data Fusion for Age and Gender Classification |
| title_short | Periocular Data Fusion for Age and Gender Classification |
| title_sort | periocular data fusion for age and gender classification |
| topic | periocular features multimodal fusion machine learning fusion strategies privacy |
| url | https://www.mdpi.com/2313-433X/8/11/307 |
| work_keys_str_mv | AT carmenbisogni perioculardatafusionforageandgenderclassification AT luciacascone perioculardatafusionforageandgenderclassification AT fabionarducci perioculardatafusionforageandgenderclassification |