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We address the use of selfie ocular images captured with smartphones toestimate age and gender. Partial face occlusion has become an issue due to themandatory use of face masks. Also, the use of mobile devices has exploded, withthe pandemic further accelerating the migration to digital services. However,state-of-the-art solutions in related tasks such as identity or expressionrecognition employ large Convolutional Neural Networks, whose use in mobiledevices is infeasible due to hardware limitations and size restrictions ofdownloadable applications. To counteract this, we adapt two existinglightweight CNNs proposed in the context of the ImageNet Challenge, and twoadditional architectures proposed for mobile face recognition. Since datasetsfor soft-biometrics prediction using selfie images are limited, we counteractover-fitting by using networks pre-trained on ImageNet. Furthermore, somenetworks are further pre-trained for face recognition, for which very largetraining databases are available. Since both tasks employ similar input data,we hypothesize that such strategy can be beneficial for soft-biometricsestimation. A comprehensive study of the effects of different pre-training overthe employed architectures is carried out, showing that, in most cases, abetter accuracy is obtained after the networks have been fine-tuned for facerecognition.

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images