The rapid development of camera in hand-held devices and the emergence of social media has led to an uprise in capturing self-portrait images. Augmenting these images for beautification or applying special effects to mimic DSLR camera has become a popular practice. Most of these effects require separation of foreground from background where the effect can be applied solely on background. To employ such effects on portrait (upper half of human body) images, a pixel-accurate segmentation is imperative. In this paper, we propose an effective method of fast depth aware CNN based portrait segmentation from monocular images. The proposed method is capable of being deployed on mobile phones, within the constraints of time and memory. On the segmented images, we demonstrate the application of bokeh effect, which blurs out-of-focus regions. We experiment with different combinations of state of the art encoder and decoder networks for segmentation and infer that our proposed method can improve the inference speed by 76 ms on mobile device while maintaining an accuracy of 97.0 %.