Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

Autor: Fernando Alonso‐Fernandez, Kevin Hernandez‐Diaz, Silvia Ramis, Francisco J. Perales, Josef Bigun
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IET Biometrics, Vol 10, Iss 5, Pp 562-580 (2021)
Druh dokumentu: article
ISSN: 2047-4946
2047-4938
DOI: 10.1049/bme2.12046
Popis: Abstract We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state‐of‐the‐art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft‐biometrics prediction using selfie images are limited, we counteract over‐fitting by using networks pre‐trained on ImageNet. Furthermore, some networks are further pre‐trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesise that such strategy can be beneficial for soft‐biometrics estimation. A comprehensive study of the effects of different pre‐training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine‐tuned for face recognition.
Databáze: Directory of Open Access Journals
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