Face Detection Coupling Texture, Color and Depth Data

Abstract

In this chapter, we propose an ensemble of face detectors for maximizing the number of true positives found by the system. Unfortunately, combining different face detectors increases both the number of true positives and false positives. To overcome this difficulty, several methods for reducing false positives are tested and proposed. The different filtering steps are based on the characteristics of the depth map related to the subwindows of the whole image that contain the candidate faces. The most simple and easiest criteria to use, for instance, is to filter the candidate face region by considering its size in metric units.\ud \ud The experimental section demonstrates that the proposed set of filtering steps greatly reduces the number of false positives without decreasing the detection rate. The proposed approach has been validated on a dataset of 549 images (each including both 2D and depth data) representing 614 upright frontal faces. The images were acquired both outdoors and indoors, with both first and second generation Kinect sensors. This was done in order to simulate a real application scenario. Moreover, for further validation and comparison with the state-of-the-art, our ensemble of face detectors is tested on the widely used BioID dataset where it obtains 100 % detection rate with an acceptable number of false positives.\ud \ud A MATLAB version of the filtering steps and the dataset used in this paper will be freely available from http://​www.​dei.​unipd.​it/​node/​2357

Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

“…The rationale behind the proposed approach is to use some filtering steps to reduce false positives. The present work extends [9] by adding to the proposed ensemble additional face detectors.…”

“…Face detection is fundamental in that it serves as the basis for many applications [1] that involve the human face, such as face alignment [2,3], face recognition/authentication [4,5,6,7], face tracking and tagging [8], etc. Face detection is a hard problem because unlike face localization, no assumptions can be made regarding whether any faces are located within an image [9,10]. Moreover, faces vary widely based on gender, age, facial expressions, and race, and can dramatically change in appearance depending on such environmental conditions as illumination, pose (out-of-plane rotation), orientation (in-plane rotation), scale, and degree of occlusion and background complexity.…”

“…Anisetti et al [54] located faces by applying a course detection method followed by a technique based on a 3D morphable face model that improves accuracy by reducing the number of false positives, and Taigman et al [6] found that combining a 3D model-based alignment with DeepFace trained on the Labeled Faces in the Wild (LFW) dataset [55] generalized well in the detection of faces in an unconstrained environment. Nanni et al [9] overcame the problem of increased false positives when combining different face detectors in an ensemble by applying different filtering steps based on information in the Kinetic depth map.…”

“…The face detection system proposed in this paper is composed of an ensemble of face detectors that utilizes information extracted from the 2D image and depth maps obtained by Microsoft’s Kinect 1 and Kinect 2 3D devices. The goal of this paper, which improves the method presented in [9], is to test a set of filters, which includes a new wave-based filter proposed here, on a new collection of face detectors. The main objective of this study is to find those filters that preserve the ensemble’s increased rate of true positives while simultaneously reducing the number of false positives.…”

“…The main objective of this study is to find those filters that preserve the ensemble’s increased rate of true positives while simultaneously reducing the number of false positives. Creating an ensemble of classifiers is a feasible method for improving performance in face detection (see [9]), as well as in many other classification problems. The main reason that ensembles improve face detection performance is that the combination of different methods increases the number of candidate windows and thus the probability of including a previously lost true positive.…”