Depth based Occlusion Detection and Localization from 3D Face Image

Abstract

Abstract-In this paper, authors have proposed two novel techniques for occlusion detection and then localization of the occluded section from a given 3D face image if occlusion is present. For both of these methods, at first, the 2.5D or range face images are created from input 3D face images. Then for detecting the occluded faces, two approaches have been followed, namely: block based and threshold based. These two methods have been investigated individually on Bosphorus database for localization of occluded portion. Bosphorus database consists of different types of occlusions, which have been considered during our research work. If 2D and 3D images are compared then 3D images provide more reliable, accurate, valid information within digitized data. In case of 2D images each point, named as pixel, is represented by a single value. One byte for gray scale and three byte for color images in a 2D grid whereas in case of 3D, there is no concept of 2D grid. Each point is represented by three values, namely X, Y and Z. The 'Z' value in X-Y plane does not contain the reflected light energy like 2D images. The facial surface's depth data is included in Z's point set. The threshold or cutoff based technique can detect the occluded faces with the accuracy 91.79% and second approach i.e. block based approach can successfully detect the same with the success rate of 99.71%. The accuracy of the proposed occlusion detection scheme has been measured as a qualitative parameter based on subjective fidelity criteria.

A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold

“…To illustrate the accuracy of the proposed multi-feature combinatorial thresholding method for occlusion localization, we compare it with the traditional methods based on directly localizing occlusion regions based on depth information [21,24,57,58]. After locating the occlusion, we directly remove the occlusion.…”

Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model

“…In (Ganguly et al, 2015c), authors have already proposed depth based occlusion detection and localization methodology. After the successful implementation of this algorithm for localizing of the occluded region on range face image, the corresponding region is excluded from the original, and remaining region is processed further for occlusion restoration and recognition purpose.…”