Face Recognition using Open CV

Abstract

Face recognition by far has been coded using java and MATLAB. Face recognition has been using a 2-D structure by far which makes it easy to crack security. So, nowadays the 3-D Structure has been trending. Earlier times we used a photograph to crack passcodes, but now it’s not possible anymore. The Face recognizing pattern involves - Face detection, Data gathering, Data comparison, and Face recognition. Python is a platform-independent language which made it easy to integrate and collaborate with this as well as other software such as Raspberry pie. The software has used open CV which is being used for other facial recognition software. This software takes several minutes and captures multiple photos until a certain specified amount of time. We used the HAAR cascade classifier to detect faces, different types of HAAR cascade classifiers are available to recognize different facial features such as to detect eyebrows, smile, nose, Side-face, Frontal face, etc. HAAR classifiers are available in the XML file which is used in our project to detect the frontalface.

In this project we used LBPH recognizer to train our photos, this data is converted to a YML file which is saved in the same folder using PICKLE. With of help of the LBPH recognizer, we extract this YML file which we have drained to detect the face. The recognizer has CONFIDENCE MATRIX. The CM has defined 60% – 90% as the boundary value for face matching, or else the face is labeled as ‘unknown’.