Abstract
The paper considers the task of obtaining a quality assessment of facial images for usage in various video surveillance systems, video analytics, and biometric identification. The accuracy of person recognition and classification depends on the quality of the input images. We consider an approach to obtaining single face image quality assessment using a neural network model, which is trained on pairs of images that are split into two possible classes: the quality of the first image is better or worse than the quality of the second one. Two modifications of the selected baseline algorithm are proposed. A face recognition system is applied to change the loss function and image and face quality attributes are used when training the model. Experimental studies of the proposed modifications show their effectiveness. The accuracy of selecting the best and worst frame is increased by 1.3% and 1.9%, respectively.
Highlights
Computer vision algorithms such as face recognition, algorithms for determining emotions, demographic characteristics and key points of a human face, are widely used in video surveillance systems, video analytics and biometric identification
Many of the recent works consider the problem of face quality assessment from a different point of view: as an indicator that reflects the usefulness of the image for the specific algorithm being used
Our approach differs from the previous ones in that the developed algorithm remains universal: it can be applied together with any other algorithm, and with the used face recognition system
Summary
Computer vision algorithms such as face recognition, algorithms for determining emotions, demographic characteristics and key points of a human face, are widely used in video surveillance systems, video analytics and biometric identification. Many of the recent works consider the problem of face quality assessment from a different point of view: as an indicator that reflects the usefulness of the image for the specific algorithm being used. Algorithms for obtaining this value use one of the existing face recognition systems, based on which either the training and test dataset is marked up [6] or the finished model is obtained directly [7], [8]. We assume that the considered approach to applying attributes for face quality assessment is more reliable than previously proposed
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