The tasks of face detection and landmark localisation are a key foundation for many facial analysis applications, while great advancements have been achieved in recent years especially there are still challenges to increase the precision of face detection. Within this paper we presents our novel method the Integrated Deep Model fusing two state-of-the-art deep learning architectures namely Faster R-CNN and a stacked hourglass glass for improved face detection precision and accurate landmark localisation. Integration is achieved through the application of a novel optimisation function and is shown in experimental evaluation to increase accuracy of face detection specifically precision by reducing false positive detection’s considerably. Our proposed Integrated Deep Model method is evaluated on the Annotated Faces In-The- Wild, Annotated Facial Landmarks in the Wild and the Face Detection Dataset and Benchmark face detection test sets and show a high level of recall and precision when compared with previously proposed methods. Landmark localisation is evaluated on the Annotated Faces In-The-Wild and 300-W test sets, this specifically focuses on localisation accuracy from detected face bounding boxes when compared with baseline evaluations using ground truth bounding boxes, our findings highlight only a very small increase in error which is more profound for the subset of facial landmarks which border the face.