Facial expression recognition has been a functioning exploration territory in the previous ten years, with developing application regions including symbol activity, neuromarketing and amiable robots. The acknowledgment of outward appearances isn't a simple issue for AI techniques, since individuals can change altogether in the manner they demonstrate their looks. Indeed, even pictures of a similar individual in a similar outward appearance can shift in splendor, foundation and present, and these varieties are underscored if thinking about various subjects (due to varieties fit as a fiddle, ethnicity among others). Albeit outward appearance acknowledgment is contemplated in the writing, few works perform reasonable assessment abstaining from blending subjects while preparing and testing the proposed calculations. Thus, outward appearance acknowledgment is as yet a difficult issue in PC vision. In this work, we propose a straightforward answer for outward appearance acknowledgment that utilizes a blend of Convolutional Neural Network and explicit picture pre-handling steps. Convolutional Neural Networks accomplish better precision with huge information. Be that as it may, there are no openly accessible datasets with adequate information for outward appearance acknowledgment with profound structures. Subsequently, to handle the issue, we apply some pre-preparing systems to extricate just demeanour explicit highlights from a face picture and investigate the introduction request of the examples amid preparing. An investigation of the effect of each picture pre-preparing task in the precision rate is displayed. The proposed strategy: accomplishes aggressive outcomes when contrasted and other outward appearance acknowledgment techniques – going up to 92% precision - it is quick to prepare, and it takes into consideration ongoing outward appearance acknowledgment with standard PCs.