Patch-based pose invariant features for single sample face recognition
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RESEARCH PAPER
Patch‑based pose invariant features for single sample face recognition Wasseem N. Ibrahem Al‑Obaydy1 · Zainab Mahmood Fadhil2 · Basheer Husham Ali1 Received: 6 March 2020 / Revised: 27 October 2020 / Accepted: 16 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Pose variation is considered as one of the major challenges that degrade the performance of face recognition systems. Existing techniques address this problem from different attitudes. However, these methods may be inefficient or impractical in the case of single sample face recognition. This article presents an automatic patch-based pose invariant feature extraction method that can handle pose variations for the aforementioned case. The proposed method extracts Gabor and histograms of oriented gradients features from landmark-based patches. The features are then concatenated, dimensionally reduced using principal component analysis, fused using canonical correlation analysis, and normalized using min-max normalization. Experimental results carried out on the FERET database have shown the outstanding performance of the proposed method compared to that of the state-of-the-art approaches. The proposed approach achieved 100% and 96% and 94.5% recognition rates for moderate and wide pose variations, respectively. Keywords Patch-based feature extraction · Single sample face recognition · Pose invariant face recognition · Gabor magnitudes · Histograms of oriented gradients
1 Introduction Face recognition has been widely studied by the vision community over the past few decades. The importance of this technology comes from its use in various applications such as law enforcement, security and access control. Recently, the research of face recognition has been directed towards the single sample face recognition (SSFR) [1]. However, recognizing human faces in the SSFR scenario is extremely challenging due to the presence of limited single reference samples in the gallery and the large sensitivity of intra-person variations for instance pose, illumination, facial expression and partial occlusion in probe images. In particular, pose variation is considered as the most complex problem * Wasseem N. Ibrahem Al‑Obaydy [email protected] Zainab Mahmood Fadhil [email protected] Basheer Husham Ali [email protected] 1
Computer Engineering Department, College of Engineering, Al-Iraqia University, Baghdad, Iraq
Computer Engineering Department, University of Technology, Baghdad, Iraq
2
that changes the out-of-plane rotations of the face resulting in self-occluded faces [2]. Such modification alters the shape and appearance of faces in a way that some discriminated facial details are lost due to self-occlusion. This loss in information leads to severe performance degradation of the frontal face recognition systems. A vast amount of pose invariant face recognition (PIFR) approaches has been introduced to address the pose variation problem from different perspectives. For comprehensive d
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