This code is used to detect your face and eyes using python along with Haarcascades.
#Introduction: Face detection is becoming one of the most interesting topics in the computer vision literature. The survey is conducted to analyze the face recognition techniques and timeline view on different methods to handle general face recognition problems. Image processing techniques focuses on two major tasks such as Improvement of pictorial information for human interpretation and processing of image data for storage, transmission and representation for autonomous machine perception. One of the common fundamental techniques that facilitate natural human-computer interaction (HCI) is face detection.
#Working: The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers (stages) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boostingtechniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. Now we begin our typical loop, the only new thing here is the creation of faces. Basically, it finds faces! We also want to find eyes, but, in a world of false positives, wouldn't it be prudent to logically make it so that we only find eyes in faces? In all seriousness, "eye detection" probably wouldn't find an eyeball laying around. Most eye detection uses the surrounding skin, eye lids, eye lashes, and eye brows to also make the detection. Thus, our next step is to break down the faces first, before getting to the eyes.Here, we're finding faces, their sizes, drawing rectangles, and noting the ROI. Next, we poke around for some eyes.we find those, we'll go ahead and make some more rectangles. Next we finish up.