{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:13:27Z","timestamp":1788354807163,"version":"build-2803163510"},"reference-count":43,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2021,8,1]]},"DOI":"10.1587\/transinf.2020bdp0010","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T22:14:46Z","timestamp":1627769686000},"page":"1239-1249","source":"Crossref","is-referenced-by-count":7,"title":["CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition"],"prefix":"10.1587","volume":"E104.D","author":[{"given":"Pengtao","family":"JIA","sequence":"first","affiliation":[{"name":"Xi'an University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"ZHAO","sequence":"additional","affiliation":[{"name":"Xi'an University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boze","family":"LI","sequence":"additional","affiliation":[{"name":"Xi'an University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Xi'an University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] P. Kumar, M. Ahirwar, and A. Deen, \u201cA Survey on Iris Recognition System,\u201d International Journal of Computer Sciences and Engineering, vol.7, no.7, pp.302-307, 2019. 10.26438\/ijcse\/v7i7.302307","DOI":"10.26438\/ijcse\/v7i7.302307"},{"key":"2","unstructured":"[2] H. Jan, A. Ali, S. Mahmood, et al., \u201cStatistical descriptors-based automatic fingerprint identification: Machine learning approaches,\u201d 2019."},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] L. Li, Y. Peng, G. Qiu, Z. Sun, and S. Liu, \u201cA survey of virtual sample generation technology for face recognition,\u201d Artificial Intelligence Review, vol.50, no.1, pp.1-20, 2018. 10.1007\/s10462-016-9537-z","DOI":"10.1007\/s10462-016-9537-z"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] P. Connor and A. Ross, \u201cBiometric recognition by gait: A survey of modalities and features,\u201d Computer Vision and Image Understanding, vol.167, pp.1-27, Feb. 2018. 10.1016\/j.cviu.2018.01.007","DOI":"10.1016\/j.cviu.2018.01.007"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] A. Kale, A. Sundaresan, A.N. Rajagopalan, N.P. Cuntoor, A.K. Roy-Chowdhury, V. Kruger, and R. Chellappa, \u201cIdentification of humans using gait,\u201d IEEE Transactions on image processing, vol.13, no.9, pp.1163-1173, 2004. 10.1109\/tip.2004.832865","DOI":"10.1109\/TIP.2004.832865"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J. Han and B. Bhanu, \u201cIndividual recognition using gait energy image,\u201d IEEE transactions on pattern analysis and machine intelligence, vol.28, no.2, pp.316-322, 2006. 10.1109\/tpami.2006.38","DOI":"10.1109\/TPAMI.2006.38"},{"key":"7","unstructured":"[7] K.J. Wang and T.Q. Yang, \u201cGait recognition method based on column mass vector and support vector machine,\u201d Computer Engineering and Applications, vol.51, no.7, pp.169-173, 2015."},{"key":"8","unstructured":"[8] X. Chen and T. Yang, \u201cGait recognition method without influence of dress and carrying,\u201d Computer Engineering and Applications, vol.52, no.5, pp.141-146, 2016."},{"key":"9","unstructured":"[9] L. Li, G. Gu, and C. Wang, \u201cFeatures fusion gait recognition by combining energy image and canonical correlation analysis,\u201d Journal of Chinese Computer Systems, vol.35, no.11, pp.2558-2561, 2014."},{"key":"10","unstructured":"[10] Y. Chai, T. Xia, and W. Han, \u201cGait recognition algorithm based on multi-featured fusion,\u201d Journal of Chinese computer Systems, vol.35, no.3, pp.636-641, 2014."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel, \u201cBackpropagation applied to handwritten zip code recognition,\u201d Neural computation, vol.1, no.4, pp.541-551, 1989. 10.1162\/neco.1989.1.4.541","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] G.E. Hinton, S. Osindero, and Y.-W. Teh, \u201cA fast learning algorithm for deep belief nets,\u201d Neural computation, vol.18, no.7, pp.1527-1554, 2006. 10.1162\/neco.2006.18.7.1527","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] G.E. Hinton and R.R. Salakhutdinov, \u201cReducing the dimensionality of data with neural networks,\u201d science, vol.313, no.5786, pp.504-507, 2006. 10.1126\/science.1127647","DOI":"10.1126\/science.1127647"},{"key":"14","unstructured":"[14] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImageNet classification with deep convolutional neural networks,\u201d International Conference on Neural Information Processing Systems, pp.1097-1105, Curran Associates Inc., 2012."},{"key":"15","unstructured":"[15] K. Leyden, M. Koller, M. Niemier, et al., \u201cKinect image processing by CNN algorithm for gait recognition,\u201d Cnna; International Workshop on Cellular Nanoscale Networks & Their Applications, VDE, 2017."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] M. Rauf, C. Song, Y. Huang, L. Wang, and N. Jia, \u201cKnowledge transfer between networks and its application on gait recognition,\u201d IEEE International Conference on Digital Signal Processing, IEEE, 2016. 10.1109\/icdsp.2016.7868606","DOI":"10.1109\/ICDSP.2016.7868606"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] Z. Wu, Y. Huang, L. Wang, X. Wang, and T. Tan, \u201cA Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs,\u201d IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.39, no.2, pp.209-226, 2016. 10.1109\/tpami.2016.2545669","DOI":"10.1109\/TPAMI.2016.2545669"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] F.M. Castro, M.J. Mar\u00edn-Jim\u00e9nez, N. Guil, and N.P.D. Blanca, \u201cMultimodal feature fusion for CNN-based gait recognition: an empirical comparison,\u201d Neural Computing and Applications, vol.32, no.17, pp.14173-14193, 2020. 10.1007\/s00521-020-04811-z","DOI":"10.1007\/s00521-020-04811-z"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] S. Ji, W. Xu, M. Yang, and K. Yu, \u201c3D convolutional neural networks for human action recognition,\u201d IEEE transactions on pattern analysis and machine intelligence, vol.35, no.1, pp.221-231, 2012. 10.1109\/tpami.2012.59","DOI":"10.1109\/TPAMI.2012.59"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, \u201cLarge-scale video classification with convolutional neural networks,\u201d Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp.1725-1732, 2014. 10.1109\/cvpr.2014.223","DOI":"10.1109\/CVPR.2014.223"},{"key":"21","unstructured":"[21] Y.M. Assael, B. Shillingford, S. Whiteson, and N. de Freitas, \u201cLipnet: Sentence-level lipreading,\u201d 2016. arXiv preprint arXiv:1611.01599"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] T. Wolf, M. Babaee, and G. Rigoll, \u201cMulti-view gait recognition using 3D convolutional neural networks,\u201d IEEE International Conference on Image Processing, pp.4165-4169, IEEE, 2016. 10.1109\/icip.2016.7533144","DOI":"10.1109\/ICIP.2016.7533144"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"24","unstructured":"[24] F. Li, M. Zhang, G. Fu, T. Qian, and D. Ji, \u201cA Bi-LSTM-RNN model for relation classification using low-cost sequence features,\u201d arXiv preprint arXiv:1608.07720, 2016."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] X. Yi, R. Li, and M. Sun, \u201cGenerating chinese classical poems with rnn encoder-decoder,\u201d Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, vol.10565, pp.211-223, Springer, Cham, 2017. 10.1007\/978-3-319-69005-6_18","DOI":"10.1007\/978-3-319-69005-6_18"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Donahue, L.A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, \u201cLong-term recurrent convolutional networks for visual recognition and description,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2625-2634, 2015. 10.1109\/cvpr.2015.7298878","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"27","unstructured":"[27] J.R. Medel and A. Savakis, \u201cAnomaly detection in video using predictive convolutional long short-term memory networks,\u201d arXiv preprint arXiv:1612.0390, 2016."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Z. Zhang, L. Tran, X. Yin, Y. Atoum, X. Liu, J. Wan, and N. Wang, \u201cGait Recognition via Disentangled Representation Learning,\u201d 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.4705-4714, 2019. 10.1109\/cvpr.2019.00484","DOI":"10.1109\/CVPR.2019.00484"},{"key":"29","unstructured":"[29] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, and I. Polosukhin, \u201cAttention Is All You Need,\u201d arXiv preprint arXiv:1706.03762, 2017."},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] J. Gao, P. Gu, Q. Ren, J. Zhang, and X. Song, \u201cAbnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network,\u201d IEEE Access, vol.7, pp.163180-163190, 2019, doi: 10.1109\/ ACCESS.2019.2950254 10.1109\/access.2019.2950254","DOI":"10.1109\/ACCESS.2019.2950254"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] L. Wang, T. Tan, H. Ning, and W. Hu, \u201cSilhoutte analysis based gait recognition for human identification,\u201d IEEE trans Pattern Analysis and Machine Intelligence(PAMI), vol.25, no.12, pp.1505-1518, 2003. 10.1109\/tpami.2003.1251144","DOI":"10.1109\/TPAMI.2003.1251144"},{"key":"32","unstructured":"[32] S. Yu, D. Tan, and T. Tan, \u201cA Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition,\u201d Proc. 18'th International Conference on Pattern Recognition (ICPR06), Hong Kong, China, Aug. 2006. 10.1109\/icpr.2006.67"},{"key":"33","unstructured":"[33] R. Fisher, S. Perkins, A. Walker, and E. Wolfart, Sobel Edge Detector, http:\/\/homepages.inf.ed.ac.uk\/rbf\/HIPR2\/sobel.htm, 2003."},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] J.D. Rodriguez, A. Perez, and J.A. Lozano, \u201cSensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, no.3, pp.569-575, March 2010. doi: 10.1109\/TPAMI.2009.187 10.1109\/tpami.2009.187","DOI":"10.1109\/TPAMI.2009.187"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] V.A. Chenarlogh and F. Razzazi, \u201cMulti-stream 3D CNN structure for human action recognition trained by limited data,\u201d IET Computer Vision, vol.13, no.3, pp.338-344, 2019. doi: 10.1049\/iet-cvi.2018. 5088 10.1049\/iet-cvi.2018.5088","DOI":"10.1049\/iet-cvi.2018.5088"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] R. Yang, S.K. Singh, M. Tavakkoli, N. Amiri, Y. Yang, M.A. Karami, and R. Rai, \u201cCNN-LSTM deep learning architecture for computer vision-based modal frequency detection,\u201d Mechanical Systems & Signal Processing, vol.144, N.PAG, 2020. doi: 10.1016\/ j.ymssp.2020.106885 10.1016\/j.ymssp.2020.106885","DOI":"10.1016\/j.ymssp.2020.106885"},{"key":"37","unstructured":"[37] Y. Feng, Y. Li, and J. Luo, \u201cLearning effective Gait features using LSTM,\u201d 2016 23rd International Conference on Pattern Recognition (ICPR), pp.325-330, 2016. 10.1109\/icpr.2016.7899654"},{"key":"38","doi-asserted-by":"publisher","unstructured":"[17] Z. Wu, Y. Huang, L. Wang, X. Wang, and T. Tan, \u201cA Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs,\u201d IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.39, no.2, pp.209-226, 2016. 10.1109\/tpami.2016.2545669","DOI":"10.1109\/TPAMI.2016.2545669"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] H. Chao, Y. He, J. Zhang, and J. Feng, \u201cGaitset: Regarding gait as a set for cross-view gait recognition,\u201d Proceedings of the AAAI Conference on Artificial Intelligence, vol.33, 2019. 10.1609\/aaai.v33i01.33018126","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] Z. Zhang, L. Tran, X. Yin, Y. Atoum, X. Liu, J. Wan, and N. Wang, \u201cGait Recognition via Disentangled Representation Learning,\u201d 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.4705-4714, 2019. 10.1109\/cvpr.2019.00484","DOI":"10.1109\/CVPR.2019.00484"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[41] X. Wang, J. Zhang, and W.Q. Yan, \u201cGait recognition using multichannel convolution neural networks,\u201d Neural Computing and Applications, vol.32, no.2, 2020. 10.1007\/s00521-019-04524-y","DOI":"10.1007\/s00521-019-04524-y"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] M. Derlatka and M. Bogdan, \u201cEnsemble kNN classifiers for human gait recognition based on ground reaction forces,\u201d International Conference on Human System Interactions, IEEE, 2015. 10.1109\/hsi.2015.7170648","DOI":"10.1109\/HSI.2015.7170648"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] T. Sharma, S.S. Dub, and B. Gupta, \u201cPerformance Analysis of ANN based Gait Recognition,\u201d International Journal of Scientific Research in Science and Technology, pp.112-125, 2018. 10.32628\/ijsrst18401123","DOI":"10.32628\/IJSRST18401123"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/8\/E104.D_2020BDP0010\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T06:07:36Z","timestamp":1628316456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/8\/E104.D_2020BDP0010\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,1]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2020bdp0010","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,1]]},"article-number":"2020BDP0010"}}