Implementation and Optimization of Face Detection Frameworkbased on OpenCV Library on Mobile Platforms using Davinci’s technology
The widespread use of mobile devices with internet connectivity has resulted in the storage and transmission of sensitive data. This has heightened the need to perform reliable user authentication on mobile devices in order to prevent an adversary from accessing such data. Biometrics, the science of recognizing individuals based on their biological and behavioral traits, has the potential to be leveraged for this purpose. Face detection and recognition (FDR) technology is one of the biometric techniques used to identify an individual. In modern FDR, the conventional pipeline consists of four stages: detect, align, represent and classify. This paper describes the implementation and optimization of Viola Jones Face Detection Framework using OpenCV on Devkit8500, which is a low power, open source single-board computer produced by Texas Instruments (TI). It uses a TI DM3730 processor which includes three modules: ARM Processor, Digital Signal Processor (DSP) and Graphics Processing Unit (GPU). The chip closely resembles TI's newest, fastest applications processor for mobile phones, the DM3730. Roles division between those modules has been carefully developed to provide maximum performance. The Viola-Jones method for face detection is faster than any of the previously published methods. Therefore the Viola-Jones face detection system can be developed as an application for a smartphone. The goal is to convey the possibility of successfully incorporating face recognition in mobile devices.
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