Open-Set Face Identification with Sequential Analysis and Out-of-Distribution Data Detection

One of the main issues in face identification is to create a real-time application with high accuracy. Images are presented by high-dimensional feature vectors that are produced by convolutional neural networks. In order to effectively process such vectors, the hierarchical algorithm was proposed in this paper that applies sequential analysis to search the nearest neighbors among the reference images of the most reliable classes selected at a certain algorithm level. Principal component analysis was also applied to select the most significant part of feature vectors. Moreover, another issue of face recognition was investigated: the lack of training data of specific types (bad quality image, different scale or illumination, children/old people, etc.), The recognition accuracy may be low for input images that are not similar to the majority of images in the dataset used to train the feature extractors. Therefore, we propose the level of preprocessing input images by detection of rare data. In this paper datasets such as VGGFace2, MS-Celeb-1M, All ages faces, Large age gap face, and different facial descriptors were used for the testing goal. Also, we provide a procedure to collect a special dataset for a given training set by using different transformations and automatic detection of anomalies.