Analysis of unsupervised learning techniques for face recognition

Abstract

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self-organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques.

A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning

“…Unsupervised learning (UL) is quite similar conceptually. Using the above simple example, the difference is that the algorithm would have to guess whether the image contains a face or not without being explicitly given the corresponding indices during the training process (Kumar et al, 2010). Of course, when designed, the algorithm is fed with some information about the task, e.g.…”