{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:02:44Z","timestamp":1784901764838,"version":"3.55.0"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00521-021-06690-4","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:03:07Z","timestamp":1641686587000},"page":"6039-6052","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Pose-invariant face recognition with multitask cascade networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5341-5440","authenticated-orcid":false,"given":"Omar","family":"Elharrouss","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor","family":"Almaadeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somaya","family":"Al-Maadeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fouad","family":"Khelifi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"key":"6690_CR1","doi-asserted-by":"crossref","unstructured":"Elharrouss O, Almaadeed N, Al-Maadeed S (2021) A Review of Video Surveillance Systems. J Vis Commun Image Represent p 103116","DOI":"10.1016\/j.jvcir.2021.103116"},{"key":"6690_CR2","doi-asserted-by":"crossref","unstructured":"Akbari Y, Almaadeed N, Al-maadeed S, Elharrouss O (2021) Applications, databases and open computer vision research from drone videos and images: a survey. Artif Intell Rev 1\u201352","DOI":"10.1007\/s10462-020-09943-1"},{"issue":"4","key":"6690_CR3","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s00371-013-0861-x","volume":"30","author":"Z Wang","year":"2014","unstructured":"Wang Z et al (2014) Low-resolution face recognition: a review. Vis Comput 30(4):359\u2013386","journal-title":"Vis Comput"},{"key":"6690_CR4","doi-asserted-by":"crossref","unstructured":"Yi D, Lei Z, Li SZ (2013) Towards pose robust face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2013.454"},{"issue":"2","key":"6690_CR5","doi-asserted-by":"publisher","first-page":"357","DOI":"10.2478\/amns.2020.2.00042","volume":"5","author":"X Yu","year":"2020","unstructured":"Yu X, Xu F (2020) Random inverse packet information and its acquisition. Appl Math Nonlinear Sci 5(2):357\u2013366","journal-title":"Appl Math Nonlinear Sci"},{"issue":"1","key":"6690_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2478\/amns.2020.1.00001","volume":"5","author":"T Xie","year":"2020","unstructured":"Xie T, Liu R, Wei Z (2020) Improvement of the fast clustering algorithm improved by k-means in the big data. Appl Math Nonlinear Sci 5(1):1\u201310","journal-title":"Appl Math Nonlinear Sci"},{"issue":"1","key":"6690_CR7","doi-asserted-by":"publisher","first-page":"47","DOI":"10.2478\/amns.2020.1.00005","volume":"5","author":"T Li","year":"2020","unstructured":"Li T, Yang W (2020) Solution to chance constrained programming problem in swap trailer transport organisation based on improved simulated annealing algorithm. Appl Math Nonlinear Sci 5(1):47\u201354","journal-title":"Appl Math Nonlinear Sci"},{"key":"6690_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.imavis.2018.09.002","volume":"79","author":"N Crosswhite","year":"2018","unstructured":"Crosswhite N et al (2018) Template adaptation for face verification and identification. Image Vis Comput 79:35\u201348","journal-title":"Image Vis Comput"},{"issue":"1","key":"6690_CR9","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1049\/iet-bmt.2018.5033","volume":"8","author":"A Abbad","year":"2018","unstructured":"Abbad A et al (2018) Application of MEEMD in post-processing of dimensionality reduction methods for face recognition. IET Biometrics 8(1):59\u201368","journal-title":"IET Biometrics"},{"key":"6690_CR10","doi-asserted-by":"crossref","unstructured":"Dadi HS, Pillutla GKM, Makkena ML (2017) Face recognition and human tracking using GMM, HOG and SVM in surveillance videos. Ann Data Sci 1\u201323","DOI":"10.1007\/s40745-017-0123-2"},{"issue":"1","key":"6690_CR11","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1109\/TMC.2017.2702634","volume":"17","author":"W Xu","year":"2018","unstructured":"Xu W et al (2018) Sensor-assisted multi-view face recognition system on smart glass. IEEE Trans Mobile Comput 17(1):197\u2013210","journal-title":"IEEE Trans Mobile Comput"},{"key":"6690_CR12","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.eswa.2018.08.047","volume":"116","author":"MK Bhowmik","year":"2019","unstructured":"Bhowmik MK et al (2019) Enhancement of robustness of face recognition system through reduced gaussianity in Log-ICA. Expert Syst Appl 116:96\u2013107","journal-title":"Expert Syst Appl"},{"issue":"5","key":"6690_CR13","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","volume":"28","author":"Y Li","year":"2019","unstructured":"Li Y et al (2019) Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Trans Image Process 28(5):2439\u20132450","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"6690_CR14","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TIP.2018.2883743","volume":"28","author":"S Ge","year":"2019","unstructured":"Ge S et al (2019) Low-resolution face recognition in the wild via selective knowledge distillation. IEEE Trans Image Process 28(4):2051\u20132062","journal-title":"IEEE Trans Image Process"},{"key":"6690_CR15","doi-asserted-by":"crossref","unstructured":"Masi I, et al (2016) Pose-aware face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.523"},{"key":"6690_CR16","doi-asserted-by":"crossref","unstructured":"Pentland A, Moghaddam B, Starner T (1994) View-based and modular eigenspaces for face recognition","DOI":"10.1109\/CVPR.1994.323814"},{"key":"6690_CR17","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2014.244"},{"key":"6690_CR18","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"6690_CR19","doi-asserted-by":"crossref","unstructured":"Taigman Y, et al (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2014.220"},{"key":"6690_CR20","doi-asserted-by":"crossref","unstructured":"Bashbaghi, S et al (2019) Deep learning architectures for face recognition in video surveillance. Deep Learning in Object Detection and Recognition. Springer, Singapore, pp 133\u2013154","DOI":"10.1007\/978-981-10-5152-4_6"},{"key":"6690_CR21","doi-asserted-by":"crossref","unstructured":"Jourabloo A, et al (2017) Pose-invariant face alignment with a single CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2017.347"},{"key":"6690_CR22","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: bmvc","DOI":"10.5244\/C.29.41"},{"key":"6690_CR23","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"6690_CR24","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.neucom.2018.05.038","volume":"310","author":"S Banerjee","year":"2018","unstructured":"Banerjee S, Das S (2018) Mutual variation of information on transfer-CNN for face recognition with degraded probe samples. Neurocomputing 310:299\u2013315","journal-title":"Neurocomputing"},{"key":"6690_CR25","doi-asserted-by":"crossref","unstructured":"Zhang MM, Shang K, Wu H (2019) Deep Compact Discriminative representation for unconstrained face recognition. Signal Process Image Commun","DOI":"10.1016\/j.image.2019.03.015"},{"key":"6690_CR26","doi-asserted-by":"crossref","unstructured":"He R et al (2018) Wasserstein CNN: learning invariant features for nir-vis face recognition. IEEE Trans Pattern Analysis Mach Intell","DOI":"10.1109\/TPAMI.2018.2842770"},{"issue":"11","key":"6690_CR27","doi-asserted-by":"publisher","first-page":"3194","DOI":"10.1109\/TCSVT.2017.2748379","volume":"28","author":"G-S Hsu","year":"2018","unstructured":"Hsu G-S et al (2018) Fast Landmark localization with 3D component reconstruction and CNN for cross-pose recognition. IEEE Trans Circuits Syst Video Technol 28(11):3194\u20133207","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6690_CR28","unstructured":"Huang GB, Learned-Miller E (2014) Labeled faces in the wild: updates and new reporting procedures. Dept. Comput. Sci., Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep, : p. 14-003"},{"key":"6690_CR29","doi-asserted-by":"crossref","unstructured":"Littwin E, Wolf L (2016) The multiverse loss for robust transfer learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3957\u20133966","DOI":"10.1109\/CVPR.2016.429"},{"key":"6690_CR30","unstructured":"Tadmor O, Wexler Y, Rosenwein T, Shalev-Shwartz S, Shashua A (2016) Learning a metric embedding for face recognition using the multibatch method. In: Proceedings of the NIPS, pp 10\u201311"},{"key":"6690_CR31","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: Proceedings of the European conference on computer vision (ECCV), pp 499\u2013515","DOI":"10.1007\/978-3-319-46478-7_31"},{"issue":"6","key":"6690_CR32","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1109\/TPAMI.2016.2582166","volume":"39","author":"D Wang","year":"2017","unstructured":"Wang D, Otto C, Jain AK (2017) Face search at scale. IEEE Trans Pattern Anal Mach Intell 39(6):1122\u20131136","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6690_CR33","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, Swami AA, Castillo CD, Chellappa R (2016) Triplet probabilistic embedding for face verification and clustering. In: 2016 IEEE 8th international conference on biometrics theory, applications and systems (BTAS), pp 1-8. IEEE","DOI":"10.1109\/BTAS.2016.7791205"},{"key":"6690_CR34","doi-asserted-by":"crossref","unstructured":"Tran L, Yin X, Liu X (2017) Disentangled representation learning GAN for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u201310","DOI":"10.1109\/CVPR.2017.141"},{"key":"6690_CR35","doi-asserted-by":"crossref","unstructured":"Peng X, Yu X, Sohn K, Metaxas DN, Chandraker M (2017) Reconstruction-based disentanglement for pose-invariant face recognition. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1\u201310","DOI":"10.1109\/ICCV.2017.180"},{"key":"6690_CR36","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.patcog.2017.06.009","volume":"71","author":"M Patacchiola","year":"2017","unstructured":"Patacchiola M, Cangelosi A (2017) Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods. Pattern Recognit 71:132\u2013143","journal-title":"Pattern Recognit"},{"key":"6690_CR37","doi-asserted-by":"crossref","unstructured":"Sreekanth P, Kulkarni U, Shetty S, S M M (2018) Head Pose Estimation using Transfer Learning, 2018 International Conference on Recent Trends in Advance Computing (ICRTAC), Chennai, India, , pp. 73-79","DOI":"10.1109\/ICRTAC.2018.8679209"},{"key":"6690_CR38","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.patrec.2018.09.023","volume":"123","author":"FHDB Zavan","year":"2019","unstructured":"Zavan FHDB, Bellon OR, Silva L, Medioni GG (2019) Benchmarking parts-based face processing in-the-wild for gender recognition and head pose estimation. Pattern Recognit Lett 123:104\u2013110","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"6690_CR39","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","volume":"41","author":"R Ranjan","year":"2017","unstructured":"Ranjan R, Patel VM, Chellappa R (2017) Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans Pattern Anal Mach Intell 41(1):121\u2013135","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6690_CR40","unstructured":"Xie W, Zisserman A (2018) Multicolumn networks for face recognition. arXiv preprint arXiv:1807.09192"},{"key":"6690_CR41","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman I, Seitz SM, Miller D, Brossard E (2016) The MegaFace benchmark: 1 million faces for recognition at scale. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.527"},{"key":"6690_CR42","doi-asserted-by":"crossref","unstructured":"Masi I, Tran AT, Hassner T, Leksut JT, Medioni G (2016) Do we really need to collect millions of faces for effective face recognition? In European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-319-46454-1_35"},{"key":"6690_CR43","doi-asserted-by":"crossref","unstructured":"Huang C, Li Y, Loy CC, Tang X (2016) Learning deep representation for imbalanced classification. In: CVPR","DOI":"10.1109\/CVPR.2016.580"},{"key":"6690_CR44","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision, pp 499\u2013515. Springer","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"6690_CR45","unstructured":"Liu W, Wen Y, Yu Z, Yang M (2016) Large-margin softmax loss for convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 507\u2013516"},{"key":"6690_CR46","doi-asserted-by":"crossref","unstructured":"Wu Y, Liu H, Li J, Fu Y (2017) Deep face recognition with center invariant loss. In: Thematic Workshop of ACM-MM","DOI":"10.1145\/3126686.3126693"},{"key":"6690_CR47","doi-asserted-by":"crossref","unstructured":"Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: Deep hypersphere embedding for face recognition. In: IEEE conf Comput Vis Pattern Recogt, volume 1","DOI":"10.1109\/CVPR.2017.713"},{"key":"6690_CR48","doi-asserted-by":"crossref","unstructured":"Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: deep hypersphere embedding for face recognition. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.713"},{"key":"6690_CR49","doi-asserted-by":"crossref","unstructured":"Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cosface: large margin cosine loss for deep face recognition. In: CVPR","DOI":"10.1109\/CVPR.2018.00552"},{"key":"6690_CR50","doi-asserted-by":"crossref","unstructured":"Zhang X, Fang Z, Wen Y, Li Z, Qiao Y (2017) Range loss for deep face recognition with long-tailed training data. In: IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.578"},{"key":"6690_CR51","doi-asserted-by":"crossref","unstructured":"Yin X, Yu X, Sohn K, Liu X, Chandraker M (2018) Feature transfer learning for deep face recognition with long-tail data, arXiv preprint, arXiv:1803.09014","DOI":"10.1109\/CVPR.2019.00585"},{"key":"6690_CR52","doi-asserted-by":"crossref","unstructured":"Zhong Y, Deng W, Wang M, et al (2019) Unequal-training for deep face recognition with long-tailed noisy data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7812\u20137821","DOI":"10.1109\/CVPR.2019.00800"},{"key":"6690_CR53","doi-asserted-by":"crossref","unstructured":"Huang C, Li Y, Chen CL, et al (2019) Deep imbalanced learning for face recognition and attribute prediction. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"6690_CR54","doi-asserted-by":"crossref","unstructured":"Zhao K, Xu J, Cheng M-M (2019) Regularface: Deep face recognition via exclusive regularization. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1136\u20131144","DOI":"10.1109\/CVPR.2019.00123"},{"key":"6690_CR55","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, et al (2019) Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4690\u20134699","DOI":"10.1109\/CVPR.2019.00482"},{"key":"6690_CR56","doi-asserted-by":"crossref","unstructured":"Liu B, Deng W, Zhong Y, et al (2019) Fair Loss: margin-Aware Reinforcement Learning for Deep Face Recognition. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 10052\u201310061","DOI":"10.1109\/ICCV.2019.01015"},{"key":"6690_CR57","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pp 815\u201382","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"6690_CR58","doi-asserted-by":"crossref","unstructured":"Elharrouss O, Almaadeed N, Al-Maadeed S (2020) February. LFR face dataset: Left-Front-Right dataset for pose-invariant face recognition in the wild. In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp 124-130). IEEE","DOI":"10.1109\/ICIoT48696.2020.9089530"},{"issue":"24","key":"6690_CR59","doi-asserted-by":"publisher","first-page":"5992","DOI":"10.1016\/j.ijleo.2015.08.084","volume":"126","author":"O Elharrouss","year":"2015","unstructured":"Elharrouss O, Moujahid D, Tairi H (2015) Motion detection based on the combining of the background subtraction and the structure\u2013texture decomposition. Optik-Int JLight Electron Opt 126(24):5992\u20135997","journal-title":"Optik-Int JLight Electron Opt"},{"key":"6690_CR60","doi-asserted-by":"crossref","unstructured":"Geusebroek J-M, van den Boomgaard R, Smeulders AWM, Dev A (2000) Color and scale: the spatial structure of color images. In: Proceeding of the 6th European conference on computer vision (ECCV), vol 1, pp 331\u2013341","DOI":"10.1007\/3-540-45054-8_22"},{"issue":"11","key":"6690_CR61","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1016\/j.cviu.2013.07.008","volume":"117","author":"H Zhou","year":"2013","unstructured":"Zhou H, Chen Y, Feng R (2013) A novel background subtraction method based on color invariants. Comput Vis Image Understand 117(11):1589\u20131597","journal-title":"Comput Vis Image Understand"},{"key":"6690_CR62","unstructured":"Simonyan K, Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"6690_CR63","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.patrec.2017.04.004","volume":"92","author":"L Wang","year":"2017","unstructured":"Wang L, Ge L, Li R et al (2017) Three-stream CNNs for action recognition. Pattern Recognit Lett 92:33\u201340","journal-title":"Pattern Recognit Lett"},{"key":"6690_CR64","unstructured":"Gourier N, Hall D, James L (2004) Crowley. Estimating face orientation from robust detection of salient facial structures. In: FG Net workshop on visual observation of deictic gestures, vol. 6, p. 7. FGnet (IST-2000-26434) Cambridge, UK"},{"key":"6690_CR65","doi-asserted-by":"crossref","unstructured":"Sengupta S et al (2016) Frontal to profile face verification in the wild. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE","DOI":"10.1109\/WACV.2016.7477558"},{"key":"6690_CR66","doi-asserted-by":"crossref","unstructured":"Wolf L, Hassner T, Maoz, FI (2011) recognition in unconstrained videos with matched background similarity. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"6690_CR67","doi-asserted-by":"crossref","unstructured":"Masi I, Rawls S, Medioni G, Natarajan P (2016) Pose-aware face recognition in the wild. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4838\u20134846","DOI":"10.1109\/CVPR.2016.523"},{"key":"6690_CR68","doi-asserted-by":"crossref","unstructured":"Lee H, et al (2017) Structure-texture decomposition of images with interval gradient. In: Computer graphics forum. Wiley Online Library","DOI":"10.1111\/cgf.12875"},{"issue":"2","key":"6690_CR69","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TIP.2017.2765830","volume":"27","author":"X Yin","year":"2018","unstructured":"Yin X, Liu X (2018) Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans Image Process 27(2):964\u2013975","journal-title":"IEEE Trans Image Process"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06690-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06690-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06690-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T14:21:20Z","timestamp":1647440480000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06690-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,9]]},"references-count":69,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6690"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06690-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,9]]},"assertion":[{"value":"6 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"I certify that there is no actual or potential conflict of interest in relation to this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"I certify that there is no financial interest in relation to this article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial interests"}}]}}