2019 |
Krizaj, Janez; Peer, Peter; Struc, Vitomir; Dobrisek, Simon Simultaneous multi-decent regression and feature learning for landmarking in depth image Journal Article In: Neural Computing and Applications, 2019, ISBN: 0941-0643. Abstract | Links | BibTeX | Tags: 3d, biometrics, depth data, face alignment, face analysis, landmarking @article{Krizaj3Docalization,Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations. |
2018 |
Banerjee, Sandipan; Brogan, Joel; Krizaj, Janez; Bharati, Aparna; RichardWebster, Brandon; Struc, Vitomir; Flynn, Patrick J.; Scheirer, Walter J. To frontalize or not to frontalize: Do we really need elaborate pre-processing to improve face recognition? Proceedings Article In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 20–29, IEEE 2018. Abstract | Links | BibTeX | Tags: face alignment, face recognition, landmarking @inproceedings{banerjee2018frontalize,Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs). While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and correctly labelled training data. If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step? To address this question, we evaluate a number of facial landmarking algorithms and a popular frontalization method to understand their effect on facial recognition performance. Additionally, we introduce a new, automatic, single-image frontalization scheme that exceeds the performance of the reference frontalization algorithm for video-to-video face matching on the Point and Shoot Challenge (PaSC) dataset. Additionally, we investigate failure modes of each frontalization method on different facial yaw using the CMU Multi-PIE dataset. We assert that the subsequent recognition and verification performance serves to quantify the effectiveness of each pose correction scheme. |
2015 |
Camgoz, Necati Cihan; Štruc, Vitomir; Gokberk, Berk; Akarun, Lale; Kindiroglu, Ahmet Alp Facial Landmark Localization in Depth Images using Supervised Ridge Descent Proceedings Article In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn, pp. 136–141, 2015. Abstract | Links | BibTeX | Tags: 3d landmarking, facial landmarking, landmark localization, landmarking, ridge regression, SDM @inproceedings{cihan2015facial,Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably |
2011 |
Štruc, Vitomir; Žganec-Gros, Jerneja; Pavešić, Nikola Principal directions of synthetic exact filters for robust real-time eye localization Proceedings Article In: Proceedings of the COST workshop on Biometrics and Identity Management (BioID), pp. 180/192, Springer-Verlag, Berlin, Heidelberg, 2011. Abstract | Links | BibTeX | Tags: ASEF, correlation filters, eye localization, face image processing, landmark localization, landmarking, PSEF @inproceedings{BioID_Struc_2011,The alignment of the facial region with a predefined canonical form is one of the most crucial steps in a face recognition system. Most of the existing alignment techniques rely on the position of the eyes and, hence, require an efficient and reliable eye localization procedure. In this paper we propose a novel technique for this purpose, which exploits a new class of correlation filters called Principal directions of Synthetic Exact Filters (PSEFs). The proposed filters represent a generalization of the recently proposed Average of Synthetic Exact Filters (ASEFs) and exhibit desirable properties, such as relatively short training times, computational simplicity, high localization rates and real time capabilities. We present the theory of PSEF filter construction, elaborate on their characteristics and finally develop an efficient procedure for eye localization using several PSEF filters. We demonstrate the effectiveness of the proposed class of correlation filters for the task of eye localization on facial images from the FERET database and show that for the tested task they outperform the established Haar cascade object detector as well as the ASEF correlation filters. |