Ensemble of different approaches for a reliable person re-identification system


In this work we propose a simple yet effective face detector that combines several face/eye detectors that possess different characteristics. Specifically, we report an extensive study for combining face/eye detectors that results in a final system we call FED that combines three face detectors that extract regions of candidate faces from an image and two approaches for eye detection: the enhanced Pictorial Structure (PS) model for coarse eye localization and a new approach proposed here (called PEC) that provides precise eye localization. PEC is an ensemble that utilizes three texture descriptors: multi-resolution local ternary patterns, local phase quantization descriptors, and patterns of oriented edge magnitudes. The extracted features are coupled with support vector machines trained on eye and non-eye samples to perform classification. The proposed framework for face detection could be considered an ad hoc integration of existing methods (the three face detectors and the PS coarse eye detector) that is combined with the proposed novel ensemble for precise eye localization (PEC). The aim of this approach is to maximize performance (not computation time). The quality of the proposed system is validated on three datasets (the well-known BioID and FERET datasets as well as a self-collected dataset). To the best of our knowledge, our system is one of the first fully automatic face detection approaches to obtain an accuracy of almost 100% on the BioID dataset (the most important benchmark dataset for frontal face detection) and 99.1% using the same dataset with only 12 false positives. A MATLAB version of our complete system for face detection can be downloaded from https://www.dei.unipd.it/node/2357.

Keywords Eye detection; face detection; texture descriptors; local phase quantization; feature combination; support vector machine.

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