Study of the characteristics of the algorithms for facial recognition

Levchuk, Sofia; Yakimenko, Alexander A.

doi:10.17212/2307-6879-2018-3-4-40-58

Cited by 1 publication

(3 citation statements)

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“…To solve the problem of face recognition on a fragment selected in the original image, the flexible comparison on graphs [4,10], hidden Markov models [12], principal component analysis [11,12], neural network methods [8,9] are used.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

“…The results of the classification of feature vectors for face images using the researched methods and the thresholding [9,14], [17] were evaluated by calculating the probability of correct classification Accuracy, precision Pr, recall Rc, and F-measure F [4,24], [25]:…”

Section: Experimental Research Of Classifiers For An Informed Selecti...mentioning

confidence: 99%

“…The first one is the face detection on the image. It is usually solved by scanning video stream frames with a window smaller than the frame size [4,5]. In this case, computational difficulties arise due to the using of windows of different locations and sizes [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative analysis of classifiers for face recognition on image fragments identified by the FaceNet neural network

Polyakova¹,

Kozak²,

Huliaieva³

2022

HAIT

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As a result of the analysis of the literature, the based methods of face recognition on fragments of color images were identified. These are flexible comparison in graphs, hidden Markov models, principal component analysis, and neural network methods. The analyzed methods of face recognition are mainly characterized by significant computational costs and low recognition performance. An exception is the neural network methods of face recognition, which, after completing the training, make it possible to obtain a high recognition performance at low computational costs. However, when changing the prototype images of faces, it often becomes necessary to redefine the network architecture and retrain the network. The specificity of neural network methods is also the complexity of selecting the network architecture and its training. Such papers are devoted to the use of neural networks only for extraction of feature vectors of face images. The classification of the obtained feature vectors is then performed by known methods, namely, thresholding, a linear support vector machine, nearest neighbors, random forest. It has been observed that the lighting conditions in which the images were obtained and the turning of the head affect the shape of the separating surface and can decrease the feature vector classification performance for face images. Therefore, to improve the classification performance, it was decided to use correlation for prototype matching, a non-linear support vector machine and logistic regression. The performed experiment showed that correlation for prototype matching of low-light face images is characterized by higher classification performance compared to the thresholding. Moreover, the use of the Pearson and Spearman correlation coefficients showed similar results, and when using the Kendall correlation coefficient, the worst classification performance was obtained compared to the Pearson and Spearman coefficients. The research of the classification performance of images of faces that differ in head turn using correlation for prototype matching, a non-linear support vector machine and logistic regression showed the following. Correlation for prototype matching is more appropriate to use with small amounts of data due to the high classification performance and low computational complexity, since a small amount of data does not require a significant number of comparisons. However, on large amounts of data, the non-linear support vector machine requires less computation and shows similar classification performance. Using the results of the experiment, the researcher can select classification methods for a specific set of face images, preliminarily representing them with feature vectors using the network FaceNet.

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