A Machine Learning Model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once you have trained the model, you can use it to reason over data that it hasn't seen before, and make predictions about those data. Machine Learning is a branch of Artificial Intelligence giving computers the ability to model various types of data using different types of algorithms. Among its various application areas, use of machine learning to perform real-world object detection has been explored heavily for PC as well as smartphones. The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are various kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method, LBPH , Fisherface method. Facial image recognition LBPH method is based on the local_x0002_binary-operator, broadly implemented in face recognition, due to its discriminating strength and calculation easiness for facial features. Haar Cascade Classifier uses LBPH algorithm for the Extraction of feature of human face. Face recognition library recognize and manipulate faces from Python or from the command line.