МЕТОДЫ УСИЛЕНИЯ ПРОЦЕДУРЫ ИДЕНТИФИКАЦИИ ПОЛЬЗОВАТЕЛЕЙ НА ОСНОВЕ ТЕХНОЛОГИИ LIVENESS DETECTION

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Biometric identification and access control systems contain methods for recognizing a subject'spersonality based on his unique physiological and behavioral characteristics. The purpose ofthis work is to develop a system for secure interaction (authentication) of participants in gamifiededucational projects, which includes countering security threats that arise when using biometric usercharacteristics. A comparative analysis of the efficiency of recognition of fake biometric samples byliveness detection methods based on the detection of sample substitution using a photo, video on adisplay, a 3D model, and a mask has been performed. During research a method of using the livenessdetection for include to a gamified educational environment system was proposed. A modification ofthe liveness detection method (hybrid method) has been proposed and a biometric identification systemin real time has been designed using the proposed method. A two-stage hybrid biometric identificationmethod has been developed based on the joint use of passive and active software methods fordetecting fake biometric samples. The method is adapted for use with a minimum number of additionaldevices, the only biometric feature scanner is a 2D-camera. The network of types two-layerperceptron, three-layer perceptron and convolutional neural network was tested. The network wastrained on the author's training examples. The position of the announcer when recording trainingexamples: the distance of the face from the camera is 60cm, the recording modes when the head isturned by 0 (look directly into the camera), 30 (the head is slightly turned to the side) and 45 (thehead is turned strongly to the side) degrees. Based on the testing results, the best recognition rateswere found in a convolutional neural network with 3 convolutional layers and 1 fully connected one.Accuracy of recognition of the spoken word is obtained up to 100% when the user's head is turned upto 30° and up to 70% - when the user's head is turned up to 45°. The FAR value of this system was1%, the FRR value was 0% for testing on 1000 samples.