Facial Key Points (FKPs) detection is an important and challenging problem in the field of computer vision, which involves detecting FKPs like centers and corners of eyes, nose tip, etc. The problem is to predict the (x, y) realvalued coordinates in the space of image pixels of the FKPs for a given face image. It finds its application in tracking faces in images and videos, analysis of facial expressions, detection of dysmorphic facial signs for medical diagnosis, face recognition, etc. Facial features vary greatly from one individual to another, and even for a single individual there is a large amount of variation due to pose, size, position, etc. The problem becomes even more challenging when the face images are taken under different illumination conditions, viewing angles, etc. In the past few years, advancements in FKPs detection are made by the application of deep convolutional neural network (DCNN), which is a special type of feed-forward neural network with shared weights and local connectivity. DCNNs have helped build state-of-the-art models for image recognition, recommender systems, natural language processing, etc. Krizhevsky et al. [1] applied DCNN in ImageNet image classification challenge and outperformed the previous state-of-the-art model for image classification. Wang et al. [2] addressed FKPs detection by first applying histogram stretching for image contrast enhancement, followed by principal component analysis for noise reduction and mean patch search algorithm with correlation scoring and mutual information scoring for predicting left and right eye centers. Sun et al. [3] estimated FKPs by using a three level convolutional neural network, where at each level, outputs of multiple networks were fused for robust and accurate estimation. Longpre et al. [4] predicted FKPs by first applying data augmentation to expand the number of training examples, followed by testing different architectures of convolutional