Vol.:(0123456789) 1 3 Evolutionary Intelligence https://doi.org/10.1007/s12065-020-00531-4 RESEARCH PAPER Patch‑based pose invariant features for single sample face recognition Wasseem N. Ibrahem Al‑Obaydy 1  · Zainab Mahmood Fadhil 2  · Basheer Husham Ali 1 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. Exist- ing techniques address this problem from diferent attitudes. However, these methods may be inefcient 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 com- munity 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-per- son variations for instance pose, illumination, facial expres- sion and partial occlusion in probe images. In particular, pose variation is considered as the most complex problem that changes the out-of-plane rotations of the face result- ing in self-occluded faces [2]. Such modifcation alters the shape and appearance of faces in a way that some discrimi- nated 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 intro- duced to address the pose variation problem from diferent perspectives. For comprehensive details on PIFR literature, the reader is referred to the recent surveys [2, 3]. However, most of the current PIFR approaches may be impractical in the SSFR scenario due to the reasons that are described at the end of Sect. 2. The main contribution of this paper is a patch-based pose invariant feature extraction method that is efciently applicable in the SSFR framework. The proposed method extracts pose invariant facial features from the landmark- based patches located at the face organs, namely eyebrows, eyes, nose, and mouth, rather than the whole face image. Since the local patches may expose relatively small out-of- plane rotations compared to the global image, extracting discriminated details from these small regions may produce pose invariant facial features. The privileges of the proposed method over the existing PIFR methods are fourfold. In the frst place, the proposed * Wasseem N. Ibrahem Al-Obaydy wasseem.nahi@aliraqia.edu.iq Zainab Mahmood Fadhil 120094@uotechnology.edu.iq Basheer Husham Ali basheer.husham@aliraqia.edu.iq 1 Computer Engineering Department, College of Engineering, Al-Iraqia University, Baghdad, Iraq 2 Computer Engineering Department, University of Technology, Baghdad, Iraq