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

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 hypothesize 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.

Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

“…They reduced the dimensionality using PCA. In [21], they utilized lightweight CNN models, such as SqueezeNet and MobileNetV2. They validated their work on the Adience dataset.…”

“…In the CW protocol, different samples of the same subject may exist in both the training and test sets. Previous studies [9][10]14,16,21] have performed 5-fold cross validation (CV) for the OW protocol. The classification performance in these studies was lower than that of other studies implemented in the CW protocol [16,19].…”

“…GC using the periocular region has received extensive research interest [9][10][11][12][13][14][15][16][17][18][19][20][21]. Some studies have employed handcrafted features [9][10][11][12][13], and others have used deep learning to complete the GC task [14][15][16][17][18][19][20][21]. However, most methods were evaluated using images captured under constrained settings.…”