000			03010nam a22002657a 4500
003			KOHA
005			20241009144550.0
008			240927d2024 cy de||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aD 428 _bM87 2024
100	1		_aMusalhi, Nasser Humaid Shinoon Al
245	1	0	_aAGE ESTIMATING USING HUMAN GAIT EXTRACTED FROM PREPROCESSED VIDEO / _cNASSER HUMAID SHINOON AL MUSALHI ; SUPERVISOR, PROF. DR. ERBUĞ ÇELEBİ
264			_c2024
300			_a139 sheets ; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering
520			_aThis research aims to address challenges in age prediction using gait analysis by analyzing human gait derived from preprocessed video data. The study identifies problems associated with the quality and preprocessing of video data, which can affect the accuracy of age estimation algorithms. The lack of comprehensive research on preprocessing methods and suitable datasets further complicates the development of reliable age estimation systems. The research aims to extract gait features, estimate human age based on gait, implement biometric gait recognition using machine learning, and evaluate the performance of the models. The results demonstrate the effectiveness of different deep learning-based models in estimating age based on gait data. The proposed Hybrid Model consistently achieves the highest accuracy, followed by DenseNet and ResNet models. The standard CNN model is the least accurate but still provides reasonable estimations among all other tasted CNN models. The Hybrid Model emerges as the most promising choice for age estimation due to its high accuracy and consistent performance. The study also addresses the issue of imbalanced class samples through the use of sparse categorical cross-entropy and class weight balance techniques. Both approaches improve the model's performance on imbalanced training and testing data, with the hybrid model maintaining high accuracy and stability. Comparing these models with state-of-the-art models, the proposed hybrid models with sparse categorical cross entropy or class weight balance outperform existing approaches in terms of accuracy. This research contributes novel approaches for preprocessing human gait images, developing hybrid models for age estimation, and utilizing sparse categorical cross entropy and class weight balance techniques to handle imbalanced class samples. The results demonstrate the effectiveness and potential of these approaches in improving the accuracy and reliability of age estimation algorithms based on human gait analysis.
650		0	_aComputer Engineering _vDissertations, Academic
700	1		_aÇelebi, Erbuğ _esupervisor
942			_2ddc _cTS
999			_c292869 _d292869