Research Article 1 Comparison of Different Neural Network Training Algorithms with Application to Face Recognition A. Vulović 1,2 *, T. Šušteršič 1,2 , V. Ranković 1 , A. Peulić 1,2 and N. Filipović 1,2 1 Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34 000, Kragujevac, Serbia 2 Bioengineering Research and Development Center (BioIRC) Prvoslava Stojanovića 6, Kragujevac, Serbia Abstract Research in the field of face recognition has been popular for several decades. With advances in technology, approaches to solving this problems haves changed. Main goal of this paper was to compare different training algorithms for neural networks and to apply them for face recognition as it is a nonlinear problem. Algorithm that we have used for face recognition problem was the Eigenface algorithm that belongs to the Principal Component Analysis (PCA) algorithms. Percentage of recognition for all the used training functions is above 90%. Keywords: face recognition, neural network, Eigenface algorithm. Received on 2 December 2016, accepted on 8 December 2017, published on 10 January 2018 Copyright © 2018 Author et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi: 10.4108/eai.10-1-2018.153550 * Corresponding author. Email:aleksandra.vulovic@kg.ac.rs 1. Introduction People have used different body traits such as voice, face, gait cycle etc. for centuries, in order to recognize other people. Although face recognition is quite intuitive for most human beings, it rather too complex for a machine vision system to be pervasively applied [1]. Two major parts of all face recognition algorithms are: (1) face detection and normalization and (2) face identification. Fully automatic algorithms are those that include both previously mentioned parts, while partially automatic algorithms only have face identification [2]. Detection and segmentation of the face areas from the background is the first step in this process [3]. With this successfully done, one of the available algorithms (or developed new) is applied in order to determine whether a face belongs to the known or not known faces of the available database. In case of a new face, a new folder with person’s photo and information is created while in case of a known face, the database needs to be updated. The results are followed by appropriate information displayed to the user (Fig. 1). Figure 1. Phases in the process of face recognition There are two basic methods for face recognition. The first method is based on the use of deformable templates and extensive mathematics for extraction of the feature vectors EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 12 2017 - 01 2018 | Volume 4 | Issue 12 | e3 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems