COMPARISON BETWEEN DEEP NEURAL NETWORK AND PRINCIPAL COMPONENT ANALYSIS ALGORITHM IN FACE RECOGNITION

Nadya Angela, Robertus Setiawan Aji Nugroho

Abstract


Face recognition is one technology that is commonly used now. Therefore, various algorithms continue to be developed to obtain maximum results with minimum costs. One of them is the Deep Neural Network or DNN algorithm. While DNN requires a large dataset to train the algorithm, another algorithm called the Principal Component Analysis (PCA) algorithm works good in a smaller dataset. These algorithms are compared to know which algorithm has the better result in given circumstances. Later the accuracy, speed, and optimality of the algorithms are analyzed. By comparing these algorithms, we could know which algorithm is preferable in given circumstances. After the experiment is done, we can assume that the two algorithms have a slight difference in terms of accuracy. Also, the time used for running the PCA implementation code is slightly longer than DNN. However, that does not mean that the PCA algorithm is not great. If the dataset to be used is limited, PCA is going to be a good choice.


Keywords


Deep Neural Network; Principal Component Analysis; Face Recognition

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References


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DOI: https://doi.org/10.24167/proxies.v5i1.12445

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