Human Facial Age Estimation: Handcrafted Features Versus Deep Features
| Autor: | Salah Eddine Bekhouche, Abdelmalik Taleb-Ahmed, Abdelkrim Ouafi, Fadi Dornaika |
|---|---|
| Přispěvatelé: | COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Biometrics
Exploit Computer science business.industry Feature extraction Access control Pattern recognition Handcrafted features Support vector machine Deep features [SPI]Engineering Sciences [physics] Support vector regression Face (geometry) Age estimation Preprocessor Social media Artificial intelligence business |
| Zdroj: | Advances in Science, Technology and Innovation Advances in Science, Technology and Innovation, Springer Nature, pp.31-37, 2021, ⟨10.1007/978-3-030-14647-4_3⟩ Advances in Science, Technology & Innovation ISBN: 9783030146467 |
| DOI: | 10.1007/978-3-030-14647-4_3⟩ |
| Popis: | International audience; In recent times, human facial age estimation topic attracted a lot of attention due to its ability to improve biometrics systems. Recently, several applications that exploit demographic attributes have emerged. These applications include: access control, re-identification in surveillance videos, integrity of face images in social media, intelligent advertising, human–computer interaction, and law enforcement. In this chapter, we present a novel approach for human facial age estimation in facial images. The proposed approach consists of the following three main stages: (1) face preprocessing; (2) feature extraction (two different kinds of features are studied: handcrafted and deep features); (3) feeding the obtained features to a linear regressor. Also, we investigate the strength and weakness of handcrafted and deep features for facial age estimation. Experiments are conducted on three public databases (FG-NET, PAL and FACES). These experiments show that both handcrafted and deep features are effective for facial age estimation. © 2021, Springer Nature Switzerland AG. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|