{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:30:34Z","timestamp":1775068234364,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China (NSFC)","award":["U1832217"],"award-info":[{"award-number":["U1832217"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are difficult to deploy on mobile devices or embedded terminals. In this paper, we propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized in terms of the network architecture and training pattern on the basis of MobileFaceNet. Regarding the network architecture, we introduce the Squeeze-and-Excitation (SE) block and propose three improved structures via a channel attention mechanism\u2014the depthwise SE module, the depthwise separable SE module, and the linear SE module\u2014which are able to learn the correlation of information between channels and assign them different weights. In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the deep network for face recognition. Finally, we obtained high-precision and lightweight face recognition models with fewer parameters and calculations that are more suitable for applications. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed methods.<\/jats:p>","DOI":"10.3390\/info12050191","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T10:41:58Z","timestamp":1619606518000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Face Recognition Based on Lightweight Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Wenting","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., and Wolf, L. (2014, January 24\u201327). Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_2","unstructured":"Huang, G.B., Mattar, M., Berg, T., and Learned-Miller, E. (2008, January 12\u201318). Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. Proceedings of the Workshop on Faces in \u2018Real-Life\u2019 Images: Detection, Alignment, and Recognition, Marseille, France."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 24\u201327). Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_4","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014). Deep learning face representation by joint identification-verification. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2015, January 7\u201312). Deeply learned face representations are sparse, selective, and robust. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"ref_6","unstructured":"Sun, Y., Liang, D., Wang, X., and Tang, X. (2015). Deepid3: Face recognition with very deep neural networks. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., and Zisserman, A. (2015, January 7\u201310). Deep Face Recognition. Proceedings of the British Machine Vision Conference, Swansea, UK.","DOI":"10.5244\/C.29.41"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., and Zisserman, A. (2018, January 15\u201319). Vggface2: A dataset for recognising faces across pose and age. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00020"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_11","unstructured":"Liu, W., Wen, Y., Yu, Z., and Yang, M. (2016). Large-margin softmax loss for convolutional neural networks. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L. (2017, January 21\u201326). Sphereface: Deep hypersphere embedding for face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","article-title":"Additive margin softmax for face verification","volume":"25","author":"Wang","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., and Zafeiriou, S. (2019, January 15\u201320). Arcface: Additive angular margin loss for deep face recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, Y., Gao, X., and Han, Z. (2018). Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices. Chinese Conference on Biometric Recognition, Springer.","DOI":"10.1007\/978-3-319-97909-0_46"},{"key":"ref_16","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Com-puter Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_20","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_21","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_24","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 27\u201328). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 27\u201328). Mnasnet: Platform-aware neural architecture search for mobil. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seoul, Korea.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Duong, C.N., Quach, K.G., Jalata, I., Le, N., and Luu, K. (2019, January 23\u201326). Mobiface: A lightweight deep learning face recognition on mobile devices. Proceedings of the 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, FL, USA.","DOI":"10.1109\/BTAS46853.2019.9185981"},{"key":"ref_30","unstructured":"Zhang, J. (2019). SeesawFaceNets: Sparse and robust face verification model for mobile platform. arXiv."},{"key":"ref_31","unstructured":"Zhang, J. (2019). Seesaw-Net: Convolution Neural Network with Uneven Group Convolution. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_33","unstructured":"Wang, M., and Deng, W. (2018). Deep face recognition: A survey. arXiv."},{"key":"ref_34","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, Y., Wang, H., Hu, H.R., Yu, L., Wang, W., and Wang, S. (2020, January 25\u201328). Triplet distillation for deep face recognition. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab.","DOI":"10.1109\/ICIP40778.2020.9190651"},{"key":"ref_36","unstructured":"Karlekar, J., Feng, J., Wong, Z.S., and Pranata, S. (2019). Deep face recognition model compression via knowledge transfer and distillation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yan, M., Zhao, M., Xu, Z., Zhang, Q., Wang, G., and Su, Z. (2019, January 27\u201328). Vargfacenet: An efficient variable group convolutional neural network for lightweight face recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00323"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_39","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J. (2016). Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint face detection and alignment using multitask cascaded convolutional networks","volume":"23","author":"Zhang","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_42","unstructured":"Yi, D., Lei, Z., Liao, S., and Li, S. (2014). Learning face representation from scratch. arXiv."},{"key":"ref_43","unstructured":"Zheng, T., and Deng, W. (2018). Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments. Beijing Univ. Posts Telecommun. Tech. Rep., 5, Available online: http:\/\/www.whdeng.cn\/CPLFW\/Cross-Pose-LFW.pdf."},{"key":"ref_44","unstructured":"Zheng, T., Deng, W., and Hu, J. (2017). Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., and Jacobs, D.W. (2016, January 7\u20139). Frontal to profile face verification in the wild. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477558"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., and Zafeiriou, S. (2017, January 21\u201326). Agedb: The first manually collected, in-the-wild age database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.250"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wolf, L., Hassner, T., and Maoz, I. (2011, January 20\u201325). Face recognition in unconstrained videos with matched background similarity. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., and Brossard, E. (2016, January 27\u201330). The megaface benchmark: 1 million faces for recognition at scale. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.527"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","article-title":"A light cnn for deep face representation with noisy labels","volume":"13","author":"Wu","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/5\/191\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:54:30Z","timestamp":1760162070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/5\/191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["info12050191"],"URL":"https:\/\/doi.org\/10.3390\/info12050191","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]}}}