Метод максимально правдоподобных рассогласований в задаче распознавания изображений на основе глубоких нейронных сетей
In this paper we focus on the image recognition problem in the case of small sample size based on the nearest neighbor rule and matching of high-dimensional feature vectors extracted with the deep convolutional neural network. We propose the novel recognition algorithm based on the maximum likelihood method for the joint density of dissimilarities between an observed image and available instances in the training set. This likelihood is estimated using the known asymptotically normally distribution of the Jensen-Shannon divergence between image features, if the latter can be treated as the probability density estimates. This asymptotic behavior is in agreement with the well-known experimental estimates of distributions of dissimilarity distances between high-dimensional vectors. The experimental study in unconstrained face recognition for the LFW (Labeled Faces in the Wild) and YTF (YouTube Faces) datasets demonstrated, that the proposed approach makes it possible to increase the recognition accuracy at 1-5% when compared with conventional classifiers.