In this Master’s degree work artificial neural networks and back propagation learning algorithm for human faces and pattern recognition are analyzed. In the second part of work artificial neural networks and their architecture and structures models are analyzed. In the third part of article the backpropagation procedure and procedures theoretical learning principle are analyzed. In the fourth part different kinds of ANN methods and patterns extracting methods in recognition, learning and classification use were researched. In this part RGB method for patterns features extraction was described. In the fifth part the requirements specification, prototype model, use case diagram, system architecture, programs modules and objects project for software realization were created. In the same part backpropagation procedures running principle was realized. After the project part was completed, a face and patterns recognition system was created. In the sixth part the created software system was tested. According to the testing results software’s recognition rate is 82,5 % using supervised learning and 82,8 % using unsupervised learning. We found using the FAR and FRR rates the ERR rate, which was 40 %. While doing the testing with changed human characteristics, the system showed 84,6 % recognition rate. This rate shows very good work of the system by a little bit changed human characteristics. Systems realization was evaluated by users as very good one. In the seventh part software’s... [to full text]

Baigiamajame magistro darbe yra apžvelgti metodai, naudojami veidų atpažinimui bei išanalizuotas jų veikimas. Apžvelgus veidų atpažinimo metodus buvo pasirinkti trys algoritmai (tikrinių veidų, Fišerio veidų ir 2D–DCT+SOM), kurie išsamiai išanalizuoti ir įgyvendinti MATLAB aplinkoje bei ištirti įvairus jų parametrai. Pagal gautus rezultatus buvo išrinktas optimalus algoritmas, tinkantis įgyvendinimui Android operacinėje sistemoje ir ten įgyvendintas. Baigiamajame darbe taip pat buvo apžvelgtos ir išanalizuotos problemos, su kuriomis susiduriama perkeliant algoritmą į Android operacinę sistemą, pateikti siūlymai algoritmo patobulinimui bei išvados. Visi užsibrėžti tikslai buvo pasiekti, o uždaviniai – išspręsti. Veido atpažinimo algoritmų tyrimas ir įgyvendinimas operacinėje Android sistemoje. Magistro baigiamasis darbas informatikos inžinerijos laipsniui. Vilniaus Gedimino technikos universitetas. Vilnius, 2012, 187 p., 49 iliustr., 6 lent., 74 bibl., 6 priedai. / The main goal of Master degree thesis is to review face recognition algorithms and analyze their performance. After this survey three face recognition algorithms (eigenfaces, fisherfaces and 2D–DCT+SOM) have been chosen for detailed analysis and investigation of their various parameters in MATLAB environment. According to the results obtained during this research only one algorithm, which is optimal for implementation in Android operating system, has been implemented on the mobile platform. This Master degree thesis also includes problems and suggestions regarding eigenface’s algorithm implementation in Android operating system, proposals for algorithm improvement and detailed conclusions. All the objectives have been achieved and all problems – solved. Analysis of face recognition algorithms and implementation in Android operating system. Master Thesis for Informatics Engineering degree. Vilnius Gediminas Technical University. Vilnius, 2012, 187 p., 49 figures, 6 tables, 74 references, 6 appendices.