It is examined how to use digital facial anthropometry for scientific and practical problems; from biometry up to its different applications including medicine, biology, genetics, pattern recognition, and criminalistics. The presented review includes a brief history of anthropometry (as a descriptive and comparative science, the subject of investigation of which is Homo sapiens) and its transformation into modern computer methods for facial anthropometry. Concepts on face morphotype and phenotype; problems on morphology and morphometry as a basic means of facial digital anthropometry; quantitative and qualitative methods for estimating facial characteristics and parameters; problems for searching for associations between gene patterns in the genome and a man’s facial features; problems for estimating facial attractiveness; correlation between facial anthropometry and “Chernov’s faces” and with cognitive computer graphics used in practical medicine; human facial phenomenon and the problems with emotions, sex, and psychical type recognition; special cases for recognizing facial images (FIs), and methods for their solutions in the frames of digital facial anthropometry and examples of its solution are presented in the paper. The prediction on the close correlation between facial anthropometry and the Internet of Things as a modern world surrounded by a person in the 21st century is determined.

We investigate a neural network–based solution for the Automatic Meter Reading detection problem, applied to analog dial gauges. We employ a convolutional neural network with a non-linear Network in Network kernel. Presently, there is a significant interest in systems for automatic detection of analog dial gauges, particularly in the energy and household sectors, but the problem is not yet sufficiently addressed in research. Our method is a universal three-level model that takes an image as an input and outputs circular bounding areas, object classes, grids of reference points for all symbols on the front panel of the device and positions of display pointers. Since all analog pointer meters have a common nature, this multi-cascade model can serve various types of devices if its capacity is sufficient. The model is using global regression for locations of symbols, which provides resilient results even for low image quality and overlapping symbols. In this work, we do not focus on the pointer location detection since it heavily depends on the shape of the pointer. We prepare training data and benchmark the algorithm with our own framework a3net, not relying on third-party neural network solutions. The experimental results demonstrate the versatility of the proposed methods, high accuracy, and resilience of reference points detection.

Subject of Research. The paper considers an algorithm for the formation of color QR-codes for facial biometrics. Methods. The methods are based on image processing procedures, linear algebra methods and are implemented in MATLAB package environment. Main Results. We present a new graphics object: a color biometric QR code and a method of forming color QR codes in biometrics tasks. An algorithm for its implementation is described, a functional diagram of the device for its implementation is shown, examples of various variants of color biometric QR codes are given. Practical Relevance. The results obtained can be used in practical biometrics and its applications, and for creation of new types of test databases for face images

This paper observes new object detector with use of convolution network with convolution kernel of NiN-type (Network in Network type). Detection means simultaneous object localization and recognition in the scene. Detector's work is possible for scenes with arbitrary size. For network learning by the supervised learning method the 100x100 pixels frames are used. Offered method has high computational efficiency; time of HD image processing with single CPU core is 300 ms. As it will be clear from the paper high level of network operation repeatability creates conditions for stream parallel data processing by GPU with estimated execution time less than 10 ms. Our method is robust to small overlapping and to the not so high quality of detected objects' images; it is end-to-end learning model; it gives the bounding rectangles and object classes for whole image at its output. In the paper the russian open database of images, received from drive recorders is used for estimation of the object detection algorithm. Similar approach is usable for detection and estimation of other types of objects such as human faces. This method extends beyond processing of single object type; simultaneous detection of objects' combination is possible. Algorithmic validation for detector's work was carried out basing on own A3Net framework without third-party neural network programs.

The problem of masked face recognition is investigated. It is shown that real masks of various shapes, textures and colors have become a problem for state-of-the-art face recognition systems. A reason for this is the lack of the necessary real training datasets. Creation of new data based on simple methods of forming masks on face images could solve this problem. An original method is proposed including the generation of various types, shapes, and colors of masks directly on the original texture of face images. The formation of the masks on the faces of individuals, on faces in group photos, and in scenes with streams of people was taken into account. Based on 100 original face images from the CUHK Face Sketch Database, a test database was created that includes more than 20,000 masked faces images which available for use. Experiments were carried out to recognize faces from the test database within the implemented four systems, among which three are state-of-the-art systems based on “deep learning” and one is deterministic system based on the cosine-transform. The performance of these systems was evaluated, the obtained results of masked face recognition were interpreted, and the masks that were a problem for selected four systems were noted. The proposed mask generation method can be used to create corpora and test databases of images with masks. The obtained results will be useful to researchers and specialists in the field of image processing and analysis.