Driver’s inattention is one of the major factors and reasons in occurrence of many road accidents and unforeseen crashes. Hence it is crucial to develop an automatic driver warning system that can send timely warning signals to the drivers. This issue involves determining the driver’s mental state that is ultimately based on the driver’s facial expressions. Automated facial emotion recognition is a recent development in the image processing domain and is the need of the hour in applications like driver warning systems. The existing methods are capable of recognizing facial emotions even when provided with a noisy signal or imperfect data, but ultimately it lacks accuracy. It is also ineffective in dealing with spontaneous emotions, and recognition. The proposed approach develops a driver warning system that extracts the facial expressions based on a novel efficient Local Octal Pattern (LOP) and effectively recognizes the facial expressions based on Deep Neural Networks, Convolutional Neural Networks (CNN). The LOP feature map serves as an input to CNN and guides in the selection of CNN learning data thereby improving and further enhancing the understanding and learning of CNN. It also has an ability to recognize both natural and spontaneous emotions, as well as image and video can be considered as an input.The experimental results consideringYawDD dataset indicates that the proposed system has been efficiently evaluated by considering the with metrics such as Precision, Recall and F-Score and thereby it is observed and inferred that the proposed system obtained a high recall rate of 96.09% in comparison with the other state-of-the-art methods