This paper discusses the Viola-Jones algorithm for face detection and its implementation based on the STM32 microcontroller. The advantages of using embedded systems in implementing personal identification systems are given: low cost due to the reduction of the element base and low power consumption. The architecture of the hardware and software system for face detection based on a multi-core microcontroller is proposed. The following requirements are put forward for the implemented facial recognition system: processing frequency of not less than 1 frame per second, output in color format, display of faces in the form of rectangular frames on the frame, refusal to use external memory modules. Cascades and features used in the classical version of the Viola-Jones algorithm are described. MB-LBP is chosen as a feature due to the efficiency of calculation and storage within low-power embedded systems due to integer single-byte results. The structure of files of trained OpenCV classifiers is described and methods for their compression and conversion for use in 32-bit systems with limited RAM and the absence of a floating-point unit are proposed. A method for optimizing an integral image using overflow calculations is described. A multicriterial optimization problem for selecting optimal parameters of an integral image is formulated and solved using the gradient descent method. The application of SIMD instructions for parallelizing the calculation of an integral image on the STM32 is described. The results of measuring the operating time of the implemented system at different stages are presented, which confirm that the previously stated requirements are met.

Keywords: face detection, microcontroller, embedded systems, Viola-Jones algorithm, MB-LBP features, classifier optimization, integral image optimization, SIMD instructions