Crispersoft has developed several prototypes based on neural networks, including successfully implemented a face recognition system on the foundation of an open-source library.
The practical application of the theory of pattern recognition is one of the most promising methods of contactless identification of a person according to his biometric data. From a technological point of view, the face recognition system is located one step above the detection system in the sense that it compares existing images or templates with newly downloaded for possible matches.
Most face recognition systems perform the basic steps of registering, capturing camera images, extracting functions, and comparing templates for subsequent setup and operation. Although facial recognition methods differ from one software vendor to another, the goal is to reduce the information to a specific database template that is compatible with high-speed search algorithms.
The last step in face recognition is to scan the database and compare the captured image with the template to get a yes or no answer. However, if the main task of the system is the identification, it must compare the image with each template in the database.
To improve quality indicators, Crispersoft software developers combined neural network techniques with local features analysis. Ideal accuracy is achieved when the largest number of images of a human face is collected. Then the program extracts certain features from the obtained set of frames and produces an average result. Neural networks require ongoing training for optimal performance, which will improve overtime as success and error data are returned to the system.
Each person has a unique face. The analysis of local features includes about 80 landmarks, such as the contours of the ears, lips, chin, tip of the nose, nose bridge, cheekbones, the distance between the eyes, etc. In the process, the system compares facial features with small deviations to account for changes in the expression, keeps records, and adds nodal points. At the same time, sunglasses, cosmetics, or facial hair do not exclude the possibility of detection and authentication. Algorithms can recognize the face as a whole and its component sections.
Today’s task is a balance of current and future expectations of consumers regarding the confidentiality of a still-developing technology, which can be used both in innovative, harmless tools and as physical or logical security measures.
It should also be understood that the more metadata self-learning artificial intelligence receives, the better it will work.
The face recognition method used by Crispersoft experts can be beneficial in various business sectors. For example, face recognition of employees is necessary as a security tool for entry. The system can protect the information in offices by allowing or denying access to sensitive data.
Thus, intelligent video surveillance works as a reliable defender, providing security in various fields.