Branch-structured detector for fast face detection using asymmetric LBP features

Abstract

Face detection has been widely used to solve many important problems. In this paper, we present a fast face detector that is more suitable to be used by a low-cost embedded system. It can obtain high performance but use less CPU and memory resources. We first present a kind of pixel-level image feature named asymmetric LBP feature (ALBP). Furthermore, we find four kinds of four-bit ALBP features that are suitable to be used to construct cascade classifiers, since the four-bit ALBP features are discriminative features and large feature pools can be generated even if a small image patch is given. Second, we propose a branch-structured face detector that is composed of three ALBP-based cascade classifiers, i.e., one deep ALBP cascade and two shallow ALBP cascades. The detection speed and performance on FDDB dataset of the branch-structured face detector have both been evaluated. Experimental results show that it runs much faster than existing face detectors run on similar platforms, and its performance is close to that of well-known non-CNN face detectors which can achieve high performance.