Optimal Face Recognition System using Haar Classifier

Human beings when born are adorned with a presence that helps them to differentiate among other individuals around ie. “Face”. Alongside speech and fingerprints, A face is significantly used in biometrics as no two individuals have the same face. Myths though say that in this world you may resemble with some facial characteristics to 7 different individuals then these facial detection and recognition algorithms help us to identify between them. Facial expressions detection, face detection, and recognition have been impactful in the research domain especially in fields of image processing. Although it might sound like a cakewalk though undoubtedly it has proven to be a complex task for a computer to detect and pose all the features of the image passed since various other variables impair the accuracy/confidence of the algorithms mainly illumination variation, the background noise of the image, low resolution, occlusion, blurriness of the image, pixel scale of the image and many more components.Considering these myths, this research is based upon face recognition algorithms and a model to identify if there is a face in an image passed, identify the number of faces, and justify with confidence value obtained which algorithm should be suggested, is most optimum for face detection and recognition among (Haar classifiers, default face recognizer, Local Binary Pattern Histogram (LBPH) face recognizers, {Adaboost algorithm}, Eigenface methods, etc.). This research particularly focuses on the use of Haar cascades and the LBPH Face recognizer algorithm as a hybrid combination for the smart voting system as an authentication approach.