Browsing by Author "Bazatbekov B."

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    2D face recognition using PCA and triplet similarity embedding
    (Bulletin of Electrical Engineering and Informatics, 2023) Bazatbekov B.; Turan C.; Kadyrov Sh.; Aitimov A.
    The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it.
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    Realization of Monoplan, NetLines, NetSphere algorithms
    (Faculty of Engineering and Natural Sciences, 2019) Bazatbekov B.
    In this work, I consider a learning algorithmis for classification tasks, called Monoplane, NetLines and NetSphere, which are all adapted for binary real input patterns. Algorithms generally helpful in classification data by self-constructing neural network, it generates new neurons to fix previous errors and stop new compilations when the output will have minimum error percent. To make realization of algorithms, that automatically construct neural networks by using appropriate methods, like backpropagation, gradient boosting, perceprtons, adaptive boosting and e.t.c To use algorithms (Monoplane, NetLines, NetSphere) to make classification of data by self-constructing neural networks.