  How can we improve the facial soft-biometric classification with help of the
human visual system? This paper explores the use of saliency which is
equivalent to the human visual system to classify Age, Gender and Facial
Expression soft-biometric for facial images. Using the Deep Multi-level Network
(ML-Net) [1] and off-the-shelf face detector [2], we propose our approach -
SAF-BAGE, which first detects the face in the test image, increases the
Bounding Box (B-Box) margin by 30%, finds the saliency map using ML-Net, with
30% reweighted ratio of saliency map, it multiplies with the input cropped face
and extracts the Convolutional Neural Networks (CNN) predictions on the
multiplied reweighted salient face. Our CNN uses the model AlexNet [3], which
is pre-trained on ImageNet. The proposed approach surpasses the performance of
other approaches, increasing the state-of-the-art by approximately 0.8% on the
widely-used Adience [28] dataset for Age and Gender classification and by
nearly 3% on the recent AffectNet [36] dataset for Facial Expression
classification. We hope our simple, reproducible and effective approach will
help ease future research in facial soft-biometric classification using
saliency.
