000			02482nam a22002657a 4500
003			KOHA
005			20241014091232.0
008			240923d2024 cy d a|| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3431 _bO59 2024
100	1		_aOnyema, Chukwuzuroke Japheth
245	1	0	_aAGE ESTIMATION FROM FACIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORK / _cCHUKWUZUROKE JAPHETH ONYEMA ; SUPERVISOR, ASSOC. PROF. DR. HUSEYIN ÖZTOPRAK
264			_c2024
300			_a45 sheets : _e+1 CD ROM _c30 cm _btables ;
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical and Electronic Engineering
520			_aIn the realm of machine learning, estimating age from photographs of individuals is a difficult challenge. It has gotten a lot of interest from academics in recent years because of its uses in fields including customized advertising, content access regulation, and surveillance systems. Building an effective age estimation network, on the other hand, presents various obstacles, including data discrepancy, the quality of facial images, and individual aging trends. This study provides an in-depth examination of the typical approaches for developing age estimate models with the use of Convolutional Neural Networks. It also addresses standard datasets for training and assessment, as well as the most recent advances in this field. The research also looks at the common assessment measures that are used to evaluate the effectiveness of age estimation algorithms. This study's main contributions are the development of an effective Convolutional neural network age estimation model with quality performance and a thorough analysis of the impact of various factors on the model's performance, such as data preprocessing, data augmentation, network architecture (substituting some convolution layer with separable convolution to help fight overfitting due to small dataset), and transfer learning. The report not only provides a survey but also highlights gaps in the currently available information and makes recommendations for further research.
650		0	_aElectrical and Electronic Engineering _vDissertations, Academic
700	1		_aÖztoprak, Huseyın _esupervisor
942			_2ddc _cTS
999			_c292816 _d292816