Facial Recognition using Machine Learning Algorithms on Raspberry Pi

Facial recognition is a non-invasive method of biometric authentication and useful for numerous applications. The real time implementation of the algorithm with adequate accuracy is required, with hardware timing into consideration. This paper deals with the implementation of machine learning algorithm for real time facial image recognition. Two dominant methods out of many facial recognition methods are discussed, simulated and implemented using Raspberry Pi. A rigorous comparative analysis is presented considering various limitations which may be the case required for innumerable application which utilize facial recognition. The drawbacks and different use cases of each method is highlighted. The facial recognition software uses algorithms to compare a digital image captured through a camera, to the stored face print so as to authenticate a person's identity. The Haar-Cascade method was one of the first methods developed for facial recognition. The HOG (Histogram of Oriented Gradients) method has worked very effectively for object recognition and thus suitable for facial recognition also. Both the methods are compared with Eigen feature-based face recognition algorithm. Various important features are experimented like speed of operation, lighting condition, frontal face profile, side profiles, distance of image, size of image etc. The facial recognition model is implemented to detect and recognize faces in real-time by means of Raspberry Pi and Pi camera for the user defined database in addition to the available databases.