Recognising people’s age from face images with convolutional neural networks

Jaka Konda (2016) Recognising people’s age from face images with convolutional neural networks. EngD thesis.

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Abstract

The diploma thesis presents the entire process of developing a solution for recognising person’s age in the image. We start with the theoretical basics of convolutional neural networks that we used to address the problem. In the practical part we start with the preparation of used datasets and continue with learning of our neural network with the chosen widely known VGG architecture. Learned model is tested on the LAP competition dataset in order to obtain results, which are comparable with the solutions of other teams. Despite somewhat simpler approach our results proved to be quite encouraging. We surpassed human performance and ranked 4th among 11 teams.

Item Type:

Thesis (EngD thesis)

Keywords:

computer vision, machine learning, neural networks, convolutional neural networks, classification, face detection, age, age classification, age recognition

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
izr. prof. dr. Peter Peer	294	Mentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537072835)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

3421

Date Deposited:

11 Aug 2016 16:40

Last Modified:

31 Aug 2016 13:28

URI:

http://eprints.fri.uni-lj.si/id/eprint/3421

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