A Comparative Study of Eigenface and Fisherface Algorithms Based on OpenCV and Sci-kit Libraries Implementations

Facial Recognition is the task of processing an image or video content in order to identify and recognize the faces of individuals. Its area of applications are wide and a lot of research efforts have been invested which led to introduction of techniques/algorithms and programming language libraries for implementation of those techniques. Facial recognition relies heavily on the use of machine learning techniques. Convolutional Neural Network (CNN), a deep learning algorithm has been successfully applied for face recognition task. However, because of its requirements, it may not be applicable in all cases. Where application scenario cannot cope with CNN, it is necessary to resort to other techniques that use traditional Machine Learning (ML) techniques. Previous studies that performed comparison on face recognition algorithms that use traditional ML techniques only disclosed the best algorithm without revealing the best image processing library used. Considering the fact that people now depend on these libraries to build face recognition systems, it is important to empirically show the best library. In this paper an experiment was conducted with aim of assessing the performance of Fisherface and Eigenface algorithms, and that of Scikit-learn and OpenCV libraries. Eigenface and Fisherface algorithms were combined with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) classifiers respectively. The algorithms were evaluated using LFW dataset, and implemented in two Python libraries for image processing Scikit-learn and OpenCV. This is to enable us determine the best performing technique/algorithm and at the same time the best library, thereby achieving dual aims. Experimental results show that Scikit-learn implementation of Fisherface with KNN recorded the highest F-score of 67.23% while the OpenCV implementation of Eigenface with SVM recorded the lowest F-score of 14.53%. Comparing the algorithms, Fisherface with SVM produced better results than Eigenface with SVM. The same story holds for Fisherface with KNN, and Eigenface with KNN. This suggests that irrespective of classifier, Fisherface outperform Eigenface in terms of accuracy of recognition. Comparing the libraries, Scikit-learn implementations of Fisherface with SVM and Eigenface with SVM, outperform the OpenCV implementation of the same algorithms. This means scikit-learn implementation produces better results than its counterpart, the OpenCV.