Localisation and segmentation of the face region is a key step and prerequisite in automated face recognition systems. Over the past few decades, there has been particular interest and efforts in finding automated, robust, accurate and reliable face detection methods. In this paper, we propose a deep learning-based method to segment face boundaries from unconstrained images. The proposed deep learning model defines the detection of face boundary formula as a combined object localisation and segmentation task. Incorporating an object localisation in a framework developed for segmentation purpose aims to improve the segmentation accuracy achieved by a fully convolutional network. The proposed model is trained on 2800 in-the-wild face images and tested on 700 images achieving dice coefficient of 95.67%, Jaccard score of 91.89%, accuracy of 98.18%, sensitivity of 96.14% and specificity of 98.76%. The experimental results demonstrate that the proposed deep learning method outperforms most of state of the art methods in automatic face detection and segmentation on a challenging dataset.