Parallelizing a Face Detection and Tracking System for Multi-Core Processors

Abstract

This paper describes how to accelerate a real-world face detection and tracking system by taking advantage of the multiple processing cores that are present in most modern CPUs. This work makes three key contributions. The first is the presentation of a highly optimized serial face detection and tracking algorithm that uses motion estimation and local search windows to achieve fast processing rates. The second is redefining the face detection process based on a set of independent face scales that can be processed in parallel on separate CPU cores while also achieving a target processing rate. The third contribution is demonstrating how multiple cores can be used to accelerate the face tracking process which provides significant speed boosts when tracking a large number of faces simultaneously. Used in a real-world application, the parallel face detector and tracker yields a 50-70% speed boost over the serial version when tested on a commodity multi-core CPU.

Memory customisations for image processing applications targeting MPSoCs

“…Chen et al [3] implement a parallelised version of a body tracking algorithm for multicore processors, where data-and functional-level parallelism are used to parallelise the algorithm. Ranjan and Malik [17] present a multicore implementation of a face detection and tracking system that uses aspects of the Viola Jones algorithm. The algorithm is parallelised by allocating an equal portion of input data to each core.…”

Custom memory architecture for multi-core implementation of face detection algorithm

“…Data and functional parallelism is used to describe fine-and coarse-grained parallelism respectively, where the former is used for parallelising the algorithm. Ranjan and Malik [9] present a multi-core implementation of a face detection and tracking system which uses the Viola Jones algorithm. The algorithm is coarse-grained parallelised by allocating no.…”