{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:08:52Z","timestamp":1772122132158,"version":"3.50.1"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T00:00:00Z","timestamp":1589846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T00:00:00Z","timestamp":1589846400000},"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 &amp; Applic"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s00521-020-05015-1","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T06:02:36Z","timestamp":1589868156000},"page":"361-391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0909-4539","authenticated-orcid":false,"given":"Muhammad","family":"Fayyaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mussarat","family":"Yasmin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Sharif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mudassar","family":"Raza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"key":"5015_CR1","doi-asserted-by":"crossref","first-page":"105740","DOI":"10.1016\/j.asoc.2019.105740","volume":"84","author":"P P\u0142awiak","year":"2019","unstructured":"P\u0142awiak P, Abdar M, Acharya UR (2019) Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring. Appl Soft Comput 84:105740","journal-title":"Appl Soft Comput"},{"key":"5015_CR2","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ins.2019.12.045","volume":"516","author":"P P\u0142awiak","year":"2020","unstructured":"P\u0142awiak P, Abdar M, P\u0142awiak J, Makarenkov V, Acharya UR (2020) DGHNL: a new deep genetic hierarchical network of learners for prediction of credit scoring. Inf Sci 516:401\u2013418","journal-title":"Inf Sci"},{"key":"5015_CR3","doi-asserted-by":"crossref","unstructured":"Fathima A, Vaidehi K (2020) Review on facial expression recognition system using machine learning techniques. In: Advances in decision sciences, image Pprocessing, security and computer vision. Springer, Cham, pp 608\u2013618","DOI":"10.1007\/978-3-030-24318-0_70"},{"key":"5015_CR4","first-page":"1","volume-title":"Advancement of machine intelligence in interactive medical image analysis","author":"P Pandey","year":"2020","unstructured":"Pandey P, Pallavi S, Pandey SC (2020) Pragmatic medical image analysis and deep learning: an emerging trend. In: Advancement of machine intelligence in interactive medical image analysis. Springer, Singapore, pp 1\u201318"},{"key":"5015_CR5","first-page":"1","volume-title":"Neural computing and applications","author":"P P\u0142awiak","year":"2019","unstructured":"P\u0142awiak P, Acharya UR (2019) Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. In: MacIntyre J (ed) Neural computing and applications. Springer, Berlin, pp 1\u201325"},{"key":"5015_CR6","doi-asserted-by":"crossref","first-page":"104923","DOI":"10.1016\/j.knosys.2019.104923","volume":"186","author":"T Tuncer","year":"2019","unstructured":"Tuncer T, Dogan S, P\u0142awiak P, Acharya UR (2019) Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl-Based Syst 186:104923","journal-title":"Knowl-Based Syst"},{"key":"5015_CR7","doi-asserted-by":"crossref","unstructured":"Cai Z, Saberian MJ, Vasconcelos N (2019) Learning complexity-aware cascades for pedestrian detection. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2019.2910514"},{"issue":"4","key":"5015_CR8","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1007\/s10044-018-0688-1","volume":"22","author":"MA Khan","year":"2018","unstructured":"Khan MA, Akram T, Sharif M, Javed MY, Muhammad N, Yasmin M (2018) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pattern Anal Appl 22(4):1377\u20131397","journal-title":"Pattern Anal Appl"},{"issue":"6","key":"5015_CR9","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.1109\/TIP.2019.2891888","volume":"28","author":"H Yao","year":"2019","unstructured":"Yao H, Zhang S, Hong R, Zhang Y, Xu C, Tian Q (2019) Deep representation learning with part loss for person re-identification. IEEE Trans Image Process 28(6):2860\u20132871","journal-title":"IEEE Trans Image Process"},{"key":"5015_CR10","doi-asserted-by":"crossref","unstructured":"Cai L, Zeng H, Zhu J, Cao J, Hou J, Cai C (2017) Multi-view joint learning network for pedestrian gender classification. In: International symposium on intelligent signal processing and communication systems (ISPACS), pp 23\u201327","DOI":"10.1109\/ISPACS.2017.8265639"},{"key":"5015_CR11","doi-asserted-by":"crossref","unstructured":"Sivabalakrishnan M, Menaka R, Jeeva S (2019) Smart video surveillance systems and identification of human behavior analysis. In: Countering cyber attacks and preserving the integrity and availability of critical systems. IGI Global, pp 64\u201397","DOI":"10.4018\/978-1-5225-8241-0.ch004"},{"key":"5015_CR12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.imavis.2018.09.001","volume":"79","author":"Y Chen","year":"2018","unstructured":"Chen Y, Duffner S, Stoian A, Dufour J-Y, Baskurt A (2018) Deep and low-level feature based attribute learning for person re-identification. Image Vis Comput 79:25\u201334","journal-title":"Image Vis Comput"},{"key":"5015_CR13","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1109\/TPAMI.2017.2669035","volume":"40","author":"Y Sun","year":"2017","unstructured":"Sun Y, Zhang M, Sun Z, Tan T (2017) Demographic analysis from biometric data: achievements, challenges, and new frontiers. IEEE Trans Pattern Anal Mach Intell 40:332\u2013351","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5015_CR14","doi-asserted-by":"crossref","first-page":"24171","DOI":"10.1109\/ACCESS.2018.2823378","volume":"6","author":"G Azzopardi","year":"2018","unstructured":"Azzopardi G, Greco A, Saggese A, Vento M (2018) Fusion of domain-specific and trainable features for gender recognition from face images. IEEE access 6:24171\u201324183","journal-title":"IEEE access"},{"key":"5015_CR15","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/978-981-13-1402-5_21","volume-title":"Data management, analytics and innovation","author":"S Mane","year":"2019","unstructured":"Mane S, Shah G (2019) Facial recognition, expression recognition, and gender identification. In: Data management, analytics and innovation. Springer, Singapore, pp 275\u2013290"},{"key":"5015_CR16","doi-asserted-by":"crossref","first-page":"333","DOI":"10.26599\/TST.2018.9010090","volume":"24","author":"J Cheng","year":"2019","unstructured":"Cheng J, Li Y, Wang J, Yu L, Wang S (2019) Exploiting effective facial patches for robust gender recognition. Tsinghua Sci Technol 24:333\u2013345","journal-title":"Tsinghua Sci Technol"},{"key":"5015_CR17","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1007\/s00500-018-03679-5","volume":"23","author":"A Geetha","year":"2019","unstructured":"Geetha A, Sundaram M, Vijayakumari B (2019) Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors. Soft Comput 23:2525\u20132535","journal-title":"Soft Comput"},{"key":"5015_CR18","doi-asserted-by":"crossref","unstructured":"Guo G, Mu G, Fu Y (2009) Gender from body: a biologically-inspired approach with manifold learning. In: Asian conference on computer vision, pp 236\u2013245","DOI":"10.1007\/978-3-642-12297-2_23"},{"key":"5015_CR19","doi-asserted-by":"crossref","unstructured":"Ng C-B, Tay Y-H, Goi B-M (2013) A convolutional neural network for pedestrian gender recognition. In: International symposium on neural networks, pp 558\u2013564","DOI":"10.1007\/978-3-642-39065-4_67"},{"key":"5015_CR20","doi-asserted-by":"crossref","unstructured":"Ahmad K, Sohail A, Conci N, De Natale F (2018) A comparative study of global and deep features for the analysis of user-generated natural disaster related images. In: IEEE 13th image, video, and multidimensional signal processing workshop (IVMSP), pp 1\u20135","DOI":"10.1109\/IVMSPW.2018.8448670"},{"key":"5015_CR21","doi-asserted-by":"crossref","unstructured":"Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197\u20132206","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"5015_CR22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jvcir.2019.05.001","volume":"62","author":"M Afifi","year":"2019","unstructured":"Afifi M, Abdelhamed A (2019) AFIF4: deep gender classification based on adaboost-based fusion of isolated facial features and foggy faces. J Vis Commun Image Represent 62:77\u201386","journal-title":"J Vis Commun Image Represent"},{"key":"5015_CR23","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10044-015-0499-6","volume":"18","author":"C-B Ng","year":"2015","unstructured":"Ng C-B, Tay Y-H, Goi B-M (2015) A review of facial gender recognition. Pattern Anal Appl 18:739\u2013755","journal-title":"Pattern Anal Appl"},{"key":"5015_CR24","unstructured":"BenAbdelkader C, Griffin P (2005) A local region-based approach to gender classi. cation from face images. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905)-workshops, pp 52\u201352"},{"key":"5015_CR25","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TIFS.2014.2359646","volume":"9","author":"E Eidinger","year":"2014","unstructured":"Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9:2170\u20132179","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"5015_CR26","first-page":"101","volume":"7","author":"HA Ahmed","year":"2016","unstructured":"Ahmed HA, Rashid TA, Sidiq A (2016) Face behavior recognition through support vector machines. Int J Adv Comput Sci Appl 7:101\u2013108","journal-title":"Int J Adv Comput Sci Appl"},{"key":"5015_CR27","doi-asserted-by":"crossref","unstructured":"Sun N, Zheng W, Sun C, Zou C, Zhao L (2006) Gender classification based on boosting local binary pattern. In: International symposium on neural networks, pp 194\u2013201","DOI":"10.1007\/11760023_29"},{"key":"5015_CR28","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.patrec.2011.05.016","volume":"33","author":"C Shan","year":"2012","unstructured":"Shan C (2012) Learning local binary patterns for gender classification on real-world face images. Pattern Recogn Lett 33:431\u2013437","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR29","doi-asserted-by":"crossref","unstructured":"Wang J-G, Li J, Yau W-Y, Sung E (2010) Boosting dense SIFT descriptors and shape contexts of face images for gender recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, pp 96\u2013102","DOI":"10.1109\/CVPRW.2010.5543238"},{"key":"5015_CR30","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.patrec.2013.04.028","volume":"36","author":"J Bekios-Calfa","year":"2014","unstructured":"Bekios-Calfa J, Buenaposada JM, Baumela L (2014) Robust gender recognition by exploiting facial attributes dependencies. Pattern Recogn Lett 36:228\u2013234","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR31","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1016\/j.patrec.2010.02.010","volume":"31","author":"LA Alexandre","year":"2010","unstructured":"Alexandre LA (2010) Gender recognition: a multiscale decision fusion approach. Pattern Recogn Lett 31:1422\u20131427","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR32","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2016.08.055","volume":"218","author":"B Patel","year":"2016","unstructured":"Patel B, Maheshwari R, Raman B (2016) Compass local binary patterns for gender recognition of facial photographs and sketches. Neurocomputing 218:203\u2013215","journal-title":"Neurocomputing"},{"key":"5015_CR33","doi-asserted-by":"crossref","unstructured":"Li X, Zhao X, Fu Y, Liu Y (2010) Bimodal gender recognition from face and fingerprint. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2590\u20132597","DOI":"10.1109\/CVPR.2010.5539969"},{"key":"5015_CR34","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.eswa.2017.03.030","volume":"80","author":"SE Bekhouche","year":"2017","unstructured":"Bekhouche SE, Ouafi A, Dornaika F, Taleb-Ahmed A, Hadid A (2017) Pyramid multi-level features for facial demographic estimation. Expert Syst Appl 80:297\u2013310","journal-title":"Expert Syst Appl"},{"key":"5015_CR35","doi-asserted-by":"crossref","unstructured":"Divate CP, Ali SZ (2018) Study of different bio-metric based gender classification systems. In: International conference on inventive research in computing applications (ICIRCA), pp 347\u2013353","DOI":"10.1109\/ICIRCA.2018.8597340"},{"key":"5015_CR36","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/978-3-319-28658-7_3","volume-title":"Advances in signal processing and intelligent recognition systems","author":"AM Ali","year":"2016","unstructured":"Ali AM, Rashid TA (2016) Kernel visual keyword description for object and place recognition. In: Advances in signal processing and intelligent recognition systems. Springer, Cham, pp 27\u201338"},{"key":"5015_CR37","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/34.1000244","volume":"24","author":"B Moghaddam","year":"2002","unstructured":"Moghaddam B, Yang M-H (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24:707\u2013711","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5015_CR38","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1109\/TPAMI.2010.208","volume":"33","author":"J Bekios-Calfa","year":"2010","unstructured":"Bekios-Calfa J, Buenaposada JM, Baumela L (2010) Revisiting linear discriminant techniques in gender recognition. IEEE Trans Pattern Anal Mach Intell 33:858\u2013864","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5015_CR39","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.neucom.2017.08.062","volume":"275","author":"M Duan","year":"2018","unstructured":"Duan M, Li K, Yang C, Li K (2018) A hybrid deep learning CNN\u2013ELM for age and gender classification. Neurocomputing 275:448\u2013461","journal-title":"Neurocomputing"},{"key":"5015_CR40","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.procs.2018.05.053","volume":"132","author":"A Dhomne","year":"2018","unstructured":"Dhomne A, Kumar R, Bhan V (2018) Gender recognition through face using deep learning. Procedia Comput Sci 132:2\u201310","journal-title":"Procedia Comput Sci"},{"key":"5015_CR41","doi-asserted-by":"crossref","unstructured":"Zhang K, Tan L, Li Z, Qiao Y (2016) Gender and smile classification using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 34\u201338","DOI":"10.1109\/CVPRW.2016.97"},{"key":"5015_CR42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2015.11.015","volume":"70","author":"J Mansanet","year":"2016","unstructured":"Mansanet J, Albiol A, Paredes R (2016) Local deep neural networks for gender recognition. Pattern Recogn Lett 70:80\u201386","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR43","doi-asserted-by":"crossref","unstructured":"Asmara RA, Masruri I, Rahmad C, Siradjuddin I, Rohadi E, Ronilaya F et al (2018) Comparative study of gait gender identification using gait energy image (GEI) and gait information image (GII). In: MATEC web of conferences, p 15006","DOI":"10.1051\/matecconf\/201819715006"},{"key":"5015_CR44","unstructured":"Hassan OMS, Abdulazeez AM, Tiryaki VM (2018) Gait-Based human gender classification using lifting 5\/3 wavelet and principal component analysis. In: International conference on advanced science and engineering (ICOASE), pp 173\u2013178"},{"issue":"20","key":"5015_CR45","doi-asserted-by":"crossref","first-page":"9496","DOI":"10.1109\/JSEN.2019.2916018","volume":"19","author":"S Bei","year":"2019","unstructured":"Bei S, Deng J, Zhen Z, Shaojing S (2019) Gender recognition via fused silhouette features based on visual sensors. IEEE Sens J 19(20):9496\u20139503","journal-title":"IEEE Sens J"},{"key":"5015_CR46","doi-asserted-by":"crossref","unstructured":"Amayeh G, Bebis G, Nicolescu M (2008) Gender classification from hand shape. In: IEEE computer society conference on computer vision and pattern recognition workshops, pp 1\u20137","DOI":"10.1109\/CVPRW.2008.4563122"},{"key":"5015_CR47","doi-asserted-by":"crossref","unstructured":"Matsumoto R, Yoshimura H, Nishiyama M, Iwai Y (2017) Feature extraction using gaze of participants for classifying gender of pedestrians in images. In: IEEE international conference on image processing (ICIP), pp 3545\u20133549","DOI":"10.1109\/ICIP.2017.8296942"},{"key":"5015_CR48","doi-asserted-by":"crossref","unstructured":"Liu T, Ye X, Sun B (2018) Clothing and carrying invariant gait-based gender recognition. In: International conference on image and video processing, and artificial intelligence, p 108360X","DOI":"10.1117\/12.2514908"},{"key":"5015_CR49","doi-asserted-by":"crossref","unstructured":"Cao L, Dikmen M, Fu Y, Huang TS (2008) Gender recognition from body. In: Proceedings of the 16th ACM international conference on multimedia, pp 725\u2013728","DOI":"10.1145\/1459359.1459470"},{"key":"5015_CR50","doi-asserted-by":"crossref","unstructured":"Collins M, Zhang J, Miller P, Wang H (2009) Full body image feature representations for gender profiling. In: IEEE 12th international conference on computer vision workshops, ICCV workshops, pp 1235\u20131242","DOI":"10.1109\/ICCVW.2009.5457467"},{"key":"5015_CR51","doi-asserted-by":"crossref","unstructured":"Geelen CD, Wijnhoven RG, Dubbelman G (2015) Gender classification in low-resolution surveillance video: in-depth comparison of random forests and SVMs. In: Video surveillance and transportation imaging applications, p 94070\u00a0M","DOI":"10.1117\/12.2077079"},{"key":"5015_CR52","doi-asserted-by":"crossref","unstructured":"Ng C-B, Tay Y-H, Goi B-M (2013) Comparing image representations for training a convolutional neural network to classify gender. In: 1st international conference on artificial intelligence, modelling and simulation, pp 29\u201333","DOI":"10.1109\/AIMS.2013.13"},{"key":"5015_CR53","doi-asserted-by":"crossref","unstructured":"Antipov G, Berrani S-A, Ruchaud N, Dugelay J-L (2015) Learned vs. hand-crafted features for pedestrian gender recognition. In: Proceedings of the 23rd ACM international conference on multimedia, pp 1263\u20131266","DOI":"10.1145\/2733373.2806332"},{"key":"5015_CR54","doi-asserted-by":"crossref","unstructured":"Raza M, Zonghai C, Rehman SU, Zhenhua G, Jikai W, Peng B (2017) Part-wise pedestrian gender recognition via deep convolutional neural networks, pp 26\u20136","DOI":"10.1049\/cp.2017.0102"},{"key":"5015_CR55","doi-asserted-by":"crossref","unstructured":"Ng C-B, Tay Y-H, Goi B-M (2017) Training strategy for convolutional neural networks in pedestrian gender classification. In: Second international workshop on pattern recognition, p 104431A","DOI":"10.1117\/12.2280487"},{"key":"5015_CR56","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.future.2018.05.002","volume":"88","author":"M Raza","year":"2018","unstructured":"Raza M, Sharif M, Yasmin M, Khan MA, Saba T, Fernandes SL (2018) Appearance based pedestrians\u2019 gender recognition by employing stacked auto encoders in deep learning. Future Gen Comput Syst 88:28\u201339","journal-title":"Future Gen Comput Syst"},{"key":"5015_CR57","doi-asserted-by":"crossref","unstructured":"Cai L, Zhu J, Zeng H, Chen J, Cai C (2018) Deep-learned and hand-crafted features fusion network for pedestrian gender recognition. In: Proceedings of ELM-2016. Springer, Berlin, pp 207\u2013215","DOI":"10.1007\/978-3-319-57421-9_17"},{"key":"5015_CR58","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1016\/j.jfranklin.2017.09.003","volume":"355","author":"L Cai","year":"2018","unstructured":"Cai L, Zhu J, Zeng H, Chen J, Cai C, Ma K-K (2018) Hog-assisted deep feature learning for pedestrian gender recognition. J Franklin Inst 355:1991\u20132008","journal-title":"J Franklin Inst"},{"issue":"4","key":"5015_CR59","first-page":"1469","volume":"22","author":"CB Ng","year":"2018","unstructured":"Ng CB, Tay Y-H, Goi B-M (2018) Pedestrian gender classification using combined global and local parts-based convolutional neural networks. Pattern Anal Appl 22(4):1469\u20131480","journal-title":"Pattern Anal Appl"},{"key":"5015_CR60","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2017.07.007","volume":"107","author":"VA Sindagi","year":"2018","unstructured":"Sindagi VA, Patel VM (2018) A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recogn Lett 107:3\u201316","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR61","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","volume":"104","author":"C Li","year":"2018","unstructured":"Li C, Guo J, Porikli F, Pang Y (2018) Lightennet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recogn Lett 104:15\u201322","journal-title":"Pattern Recogn Lett"},{"key":"5015_CR62","doi-asserted-by":"crossref","first-page":"15751","DOI":"10.1007\/s11042-018-7031-0","volume":"78","author":"M Rashid","year":"2019","unstructured":"Rashid M, Khan MA, Sharif M, Raza M, Sarfraz MM, Afza F (2019) Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools Appl 78:15751\u201315777","journal-title":"Multimedia Tools Appl"},{"key":"5015_CR63","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compag.2018.10.013","volume":"155","author":"MA Khan","year":"2018","unstructured":"Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H et al (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 155:220\u2013236","journal-title":"Comput Electron Agric"},{"key":"5015_CR64","doi-asserted-by":"crossref","unstructured":"Sharif M, Attique Khan M, Rashid M, Yasmin M, Afza F, Tanik UJ (2019) Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exp Theor Artif Intell, 1\u201323","DOI":"10.1080\/0952813X.2019.1572657"},{"key":"5015_CR65","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.procs.2018.10.307","volume":"140","author":"TA Rashid","year":"2018","unstructured":"Rashid TA, Fattah P, Awla DK (2018) Using accuracy measure for improving the training of lstm with metaheuristic algorithms. Procedia Computer Science 140:324\u2013333","journal-title":"Procedia Computer Science"},{"key":"5015_CR66","doi-asserted-by":"crossref","unstructured":"Rashid TA (2016) Convolutional neural networks based method for improving facial expression recognition. In: The international symposium on intelligent systems technologies and applications, pp 73\u201384","DOI":"10.1007\/978-3-319-47952-1_6"},{"key":"5015_CR67","doi-asserted-by":"crossref","first-page":"31","DOI":"10.25079\/ukhjse.v3n2y2019.pp31-40","volume":"3","author":"AS Shamsaldin","year":"2019","unstructured":"Shamsaldin AS, Fattah P, Rashid TA, Al-Salihi NK (2019) A study of the convolutional neural networks applications. UKH J Sci Eng 3:31\u201340","journal-title":"UKH J Sci Eng"},{"key":"5015_CR68","doi-asserted-by":"crossref","first-page":"e0213237","DOI":"10.1371\/journal.pone.0213237","volume":"14","author":"TA Rashid","year":"2019","unstructured":"Rashid TA, Abbas DK, Turel YK (2019) A multi hidden recurrent neural network with a modified grey wolf optimizer. PLoS ONE 14:e0213237","journal-title":"PLoS ONE"},{"key":"5015_CR69","first-page":"55","volume":"6","author":"TA Rashid","year":"2018","unstructured":"Rashid TA, Abdullah SM (2018) A hybrid of artificial bee colony, genetic algorithm, and neural network for diabetic mellitus diagnosing. ARO Sci J Koya Univ 6:55\u201364","journal-title":"ARO Sci J Koya Univ"},{"key":"5015_CR70","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.3390\/s19071599","volume":"19","author":"MA Uddin","year":"2019","unstructured":"Uddin MA, Lee Y-K (2019) Feature fusion of deep spatial features and handcrafted spatiotemporal features for human action recognition. Sensors 19:1599","journal-title":"Sensors"},{"key":"5015_CR71","doi-asserted-by":"crossref","first-page":"102659","DOI":"10.1016\/j.jvcir.2019.102659","volume":"65","author":"X Fan","year":"2019","unstructured":"Fan X, Tjahjadi T (2019) Fusing dynamic deep learned features and handcrafted features for facial expression recognition. J Vis Commun Image Represent 65:102659","journal-title":"J Vis Commun Image Represent"},{"key":"5015_CR72","doi-asserted-by":"crossref","first-page":"64827","DOI":"10.1109\/ACCESS.2019.2917266","volume":"7","author":"M-I Georgescu","year":"2019","unstructured":"Georgescu M-I, Ionescu RT, Popescu M (2019) Local learning with deep and handcrafted features for facial expression recognition. IEEE Access 7:64827\u201364836","journal-title":"IEEE Access"},{"key":"5015_CR73","doi-asserted-by":"crossref","first-page":"79959","DOI":"10.1109\/ACCESS.2019.2922691","volume":"7","author":"AM Hasan","year":"2019","unstructured":"Hasan AM, Jalab HA, Meziane F, Kahtan H, Al-Ahmad AS (2019) Combining deep and handcrafted image features for MRI brain scan classification. IEEE Access 7:79959\u201379967","journal-title":"IEEE Access"},{"key":"5015_CR74","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol 1. IEEE, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"5015_CR75","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.neucom.2015.12.140","volume":"213","author":"X Wang","year":"2016","unstructured":"Wang X, Zhao C, Miao D, Wei Z, Zhang R, Ye T (2016) Fusion of multiple channel features for person re-identification. Neurocomputing 213:125\u2013136","journal-title":"Neurocomputing"},{"key":"5015_CR76","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00521-012-0846-0","volume":"23","author":"Z Qi","year":"2013","unstructured":"Qi Z, Tian Y, Shi Y (2013) Efficient railway tracks detection and turnouts recognition method using HOG features. Neural Comput Appl 23:245\u2013254","journal-title":"Neural Comput Appl"},{"key":"5015_CR77","doi-asserted-by":"crossref","unstructured":"Chee KW, Teoh SS (2019) Pedestrian detection in visual images using combination of HOG and HOM features. In: 10th international conference on robotics, vision, signal processing and power applications, pp. 591\u2013597","DOI":"10.1007\/978-981-13-6447-1_75"},{"key":"5015_CR78","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.matcom.2017.12.011","volume":"155","author":"Y Wei","year":"2019","unstructured":"Wei Y, Tian Q, Guo J, Huang W, Cao J (2019) Multi-vehicle detection algorithm through combining Harr and HOG features. Math Comput Simul 155:130\u2013145","journal-title":"Math Comput Simul"},{"key":"5015_CR79","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/TPWRD.2018.2872820","volume":"34","author":"K Firuzi","year":"2018","unstructured":"Firuzi K, Vakilian M, Phung BT, Blackburn TR (2018) Partial discharges pattern recognition of transformer defect model by LBP & HOG features. IEEE Trans Power Delivery 34:542\u2013550","journal-title":"IEEE Trans Power Delivery"},{"key":"5015_CR80","doi-asserted-by":"crossref","unstructured":"Xiao T, Li S, Wang B, Lin L, Wang X (2017) Joint detection and identification feature learning for person search. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3415\u20133424","DOI":"10.1109\/CVPR.2017.360"},{"key":"5015_CR81","first-page":"4198","volume-title":"Advances in neural information processing systems","author":"J Xu","year":"2018","unstructured":"Xu J, Luo L, Deng C, Huang H (2018) Bilevel distance metric learning for robust image recognition. In: Advances in neural information processing systems, pp 4198\u20134207"},{"key":"5015_CR82","doi-asserted-by":"crossref","unstructured":"Junejo IN (2019) A deep learning based multi-color space approach for pedestrian attribute recognition. In: Proceedings of the 2019 3rd international conference on graphics and signal processing, pp 113\u2013116","DOI":"10.1145\/3338472.3338493"},{"key":"5015_CR83","doi-asserted-by":"crossref","unstructured":"Ren Q-Q, Tian W-D, Zhao Z-Q (2019) \u201cPerson re-identification based on feature fusion. In: International Conference on Intelligent Computing, 2019, pp. 65-73","DOI":"10.1007\/978-3-030-26766-7_7"},{"key":"5015_CR84","doi-asserted-by":"crossref","unstructured":"Liao S, Zhao G, Kellokumpu V, Pietik\u00e4inen M, Li SZ (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE computer society conference on computer vision and pattern recognition, pp 1301\u20131306","DOI":"10.1109\/CVPR.2010.5539817"},{"key":"5015_CR85","doi-asserted-by":"crossref","unstructured":"AroraS, Bhatia M (2018) A robust approach for gender recognition using deep learning. In: 9th international conference on computing, communication and networking technologies (ICCCNT), pp 1\u20136","DOI":"10.1109\/ICCCNT.2018.8494194"},{"key":"5015_CR86","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","volume":"126","author":"RD Labati","year":"2018","unstructured":"Labati RD, Mu\u00f1oz E, Piuri V, Sassi R, Scotti F (2018) Deep-ECG: convolutional neural networks for ECG biometric recognition. Pattern Recognit Lett 126:78\u201385","journal-title":"Pattern Recognit Lett"},{"key":"5015_CR87","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2019.03.006","volume":"125","author":"E-J Cheng","year":"2019","unstructured":"Cheng E-J, Chou K-P, Rajora S, Jin B-H, Tanveer M, Lin C-T et al (2019) Deep Sparse Representation Classifier for facial recognition and detection system. Pattern Recognit Lett 125:71\u201377","journal-title":"Pattern Recognit Lett"},{"key":"5015_CR88","doi-asserted-by":"crossref","unstructured":"Fayyaz M, Yasmin M, Sharif M, Shah JH, Raza M, Iqbal T (2019) Person re-identification with features-based clustering and deep features. Neural Comput Appl, 1\u201322","DOI":"10.1007\/s00521-019-04590-2"},{"key":"5015_CR89","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","volume":"7","author":"F Hu","year":"2015","unstructured":"Hu F, Xia G-S, Hu J, Zhang L (2015) Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 7:14680\u201314707","journal-title":"Remote Sens"},{"key":"5015_CR90","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5015_CR91","unstructured":"Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, pp 61\u201367"},{"key":"5015_CR92","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.aca.2018.09.022","volume":"1063","author":"CL Morais","year":"2019","unstructured":"Morais CL, Lima KM, Martin FL (2019) Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines. Anal Chim Acta 1063:40\u201346","journal-title":"Anal Chim Acta"},{"key":"5015_CR93","doi-asserted-by":"crossref","unstructured":"Radhika K, Varadarajan S (2018) Ensemble subspace discriminant classification of satellite images","DOI":"10.1109\/ICIMIA.2017.7975545"},{"key":"5015_CR94","doi-asserted-by":"crossref","unstructured":"Shirkhorshidi AS, Aghabozorgi S, Wah TY (2015) A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS One 10(12)","DOI":"10.1371\/journal.pone.0144059"},{"key":"5015_CR95","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.image.2017.08.001","volume":"58","author":"K Lekdioui","year":"2017","unstructured":"Lekdioui K, Messoussi R, Ruichek Y, Chaabi Y, Touahni R (2017) Facial decomposition for expression recognition using texture\/shape descriptors and SVM classifier. Sig Process Image Commun 58:300\u2013312","journal-title":"Sig Process Image Commun"},{"key":"5015_CR96","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1016\/j.patcog.2011.09.021","volume":"45","author":"X-X Niu","year":"2012","unstructured":"Niu X-X, Suen CY (2012) A novel hybrid CNN\u2013SVM classifier for recognizing handwritten digits. Pattern Recognit 45:1318\u20131325","journal-title":"Pattern Recognit"},{"key":"5015_CR97","doi-asserted-by":"crossref","unstructured":"Deng Y, Luo P, Loy CC, Tang X (2014) Pedestrian attribute recognition at far distance. In: Proceedings of the 22nd ACM international conference on multimedia, pp 789\u2013792","DOI":"10.1145\/2647868.2654966"},{"key":"5015_CR98","doi-asserted-by":"crossref","unstructured":"Zhu W, Miao J, Qing L, Huang G-B (2015) Hierarchical extreme learning machine for unsupervised representation learning. In: International joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280669"},{"key":"5015_CR99","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5015_CR100","doi-asserted-by":"crossref","first-page":"840","DOI":"10.3390\/s19040840","volume":"19","author":"Q Wang","year":"2019","unstructured":"Wang Q, Ye L, Luo H, Men A, Zhao F, Huang Y (2019) Pedestrian stride-length estimation based on LSTM and denoising autoencoders. Sensors 19:840","journal-title":"Sensors"},{"key":"5015_CR101","first-page":"4","volume":"6","author":"T Rashid","year":"2018","unstructured":"Rashid T, Jabar A (2018) A modified particle swarm optimization with neural network via Euclidean distance. Int J Recent Contrib Eng Sci IT (IJES) 6:4\u201318","journal-title":"Int J Recent Contrib Eng Sci IT (IJES)"},{"key":"5015_CR102","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","volume":"11","author":"M Haghighat","year":"2016","unstructured":"Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans Inf Forensics Secur 11:1984\u20131996","journal-title":"IEEE Trans Inf Forensics Secur"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05015-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05015-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05015-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T23:46:34Z","timestamp":1621381594000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05015-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,19]]},"references-count":102,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["5015"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05015-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,19]]},"assertion":[{"value":"1 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}