Reliability of camera systems to recognize facial features for access to specialized production areas

The article deals with ergonomics and reliability of camera systems for recognition of facial features and identify person for access to specialized areas. The monitoring of areas relates not only to crime, but it is also an integral part of access to specialized production areas (pharmaceutical production, chemical production, specialized food production, etc.). It is therefore important to adequately secure these premises using the relevant system. One of them is a system based on user identification using specific facial features. For this purpose, there are CCTV systems for recognition of facial features of different price categories (conventional cameras, semi-professional and professional) on the world market. However, problematic situations may occur when identifying. For example, by having the user partially masked face. This research is focusing on the problem. The main goal of the research is establishing the scale of negative impact, in case the identified person has partially masked face, on camera systems recognizing facial features, primarily on recognition time. The results are evaluated in detail. Some camera systems are not suitable in specialized production areas due to their insufficient recognition ability. From all the tested devices, the HIKVISION iDS-2CD8426G0 / F-I camera identification system has proved to be optimal for identification purposes. In the case of designing, it is therefore necessary to choose suitable camera systems that have ergonomics and reliability at a level that will guarantee their sufficient use in the mentioned areas, while decreasing comfort and user-friendliness as little as possible. By measuring the ergonomics and reliability of these CCTV systems, it can be stated that there are statistically significant differences between conventional, semi-professional and professional systems, and it’s not just a design change, but also a more efficient recognition method

Text detection and recognition based on a lensless imaging system

Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detection and text recognition. To address the problem, a framework of deep-learning-based pipeline structure was built to recognize text with three steps from raw data captured by employing lensless cameras. This pipeline structure consisted of the lensless imaging model U-Net, the text detection model connectionist text proposal network (CTPN), and the text recognition model convolutional recurrent neural network (CRNN). Compared with the method focusing only on image reconstruction, UNet in the pipeline was able to supplement the imaging details by enhancing factors related to character categories in the reconstruction process, so the textual information can be more effectively detected and recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed lensless images. By performing experiments on datasets of different complexities, the applicability to text detection and recognition on lensless cameras was verified. This study reasonably demonstrates text detection and recognition tasks in the lensless camera system,and develops a basic method for novel applications