Abstract
Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.
Highlights
With the rapid advancement in three-dimensional (3-D) imaging and acquisition techniques, 3-D shape analysis and processing such as 3-D surface registration and shape retrieval have been extensively studied in the recent past
Classification: The subject-specific descriptors (SSDs) si of the probe face image are matched to the gallery using four parallel classification algorithms which are based on Euclidean (Euc), Manhattan (Man), Cosine (Cos), and Mahalanobis Cosine (MahCos) distances summarized as follows
In order to demonstrate the effectiveness of the proposed 3D face recognition approach, the experimental evaluation is realized on two public 3-D face databases, namely, FRGC v2.07 and GavabDB,[8] and results are given as rank-1 recognition rates
Summary
With the rapid advancement in three-dimensional (3-D) imaging and acquisition techniques, 3-D shape analysis and processing such as 3-D surface registration and shape retrieval have been extensively studied in the recent past. The objective of the proposed study is to realize (i) an efficient face registration algorithm based on variance of facial surfaces and (ii) a face recognition approach employing nine subject-specific descriptors (SSDs) and four parallel classification algorithms. The first contribution of the proposed study is based on the literature gap in the domain of 3-D face registration (“3-D face registration” section). 2. The second contribution of the proposed approach is based on the following literature gaps in the research area of 3-D face recognition (“3D face recognition” section): (i) The existing algorithms did not propose any face registration algorithm and presented only face recognition algorithms. The proposed approach is based on a novel and computationally efficient face registration algorithm and employs nine SSDs and four parallel classification algorithms. The fifth section presents results-related discussion whereas the sixth section concludes the findings and presents future work
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