  In recent years, periocular recognition has been developed as a valuable
biometric identification approach, especially in wild environments (for
example, masked faces due to COVID-19 pandemic) where facial recognition may
not be applicable. This paper presents a new deep periocular recognition
framework called attribute-based deep periocular recognition (ADPR), which
predicts soft biometrics and incorporates the prediction into a periocular
recognition algorithm to determine identity from periocular images with high
accuracy. We propose an end-to-end framework, which uses several shared
convolutional neural network (CNN)layers (a common network) whose output feeds
two separate dedicated branches (modality dedicated layers); the first branch
classifies periocular images while the second branch predicts softn biometrics.
Next, the features from these two branches are fused together for a final
periocular recognition. The proposed method is different from existing methods
as it not only uses a shared CNN feature space to train these two tasks
jointly, but it also fuses predicted soft biometric features with the
periocular features in the training step to improve the overall periocular
recognition performance. Our proposed model is extensively evaluated using four
different publicly available datasets. Experimental results indicate that our
soft biometric based periocular recognition approach outperforms other
state-of-the-art methods for periocular recognition in wild environments.
