{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:24:03Z","timestamp":1780053843555,"version":"3.54.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319979083","type":"print"},{"value":"9783319979090","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-97909-0_46","type":"book-chapter","created":{"date-parts":[[2018,8,8]],"date-time":"2018-08-08T04:02:46Z","timestamp":1533700966000},"page":"428-438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":592,"title":["MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices"],"prefix":"10.1007","author":[{"given":"Sheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,8,9]]},"reference":[{"key":"46_CR1","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs\/1704.04861 (2017)"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR, abs\/1707.01083 (2017)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. CoRR, abs\/1801.04381 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"46_CR4","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: Ms-celeb-1\u00a0m: A dataset and benchmark for large-scale face recognition, arXiv preprint (2016). arXiv:1607.08221"},{"key":"46_CR5","unstructured":"Deng, J., Guo, J., Zafeiriou, S.: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. arXiv preprint (2018). arXiv:1801.07698"},{"key":"46_CR6","unstructured":"Huang, G.B., Ramesh, M., Berg, T., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2007)"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.527"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: The first manually collected in-the-wild age database. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"46_CR9","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5\u00a0MB model size, arXiv preprint (2016). arXiv:1602.07360"},{"key":"46_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"46_CR12","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"46_CR13","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition, arXiv preprint (2017). arXiv:1707.07012"},{"key":"46_CR14","unstructured":"Wu, X., He, R., Sun, Z., Tan, T.: A light cnn for deep face representation with noisy labels, arXiv preprint (2016). arXiv:1511.02683"},{"key":"46_CR15","unstructured":"Wu, B., Wan, A., Yue, X., Jin, P., Zhao, S., Golmant, N., et al.: Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions, arXiv preprint (2017). arXiv:1711.08141"},{"key":"46_CR16","unstructured":"Hinton, G. E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). arXiv:1503.02531"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Luo, P., Zhu, Z., Liu, Z., Wang, X., Tang, X., Luo, P., et al.: Face Model Compression by Distilling Knowledge from Neurons. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.10449"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"46_CR19","first-page":"1601","volume":"2","author":"J Long","year":"2014","unstructured":"Long, J., Zhang, N., Darrell, T.: Do convnets learn correspondence? Adv. Neural. Inf. Process. Syst. 2, 1601\u20131609 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.713"},{"issue":"7","key":"46_CR21","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Proc. Lett. 25(7), 926\u2013930 (2018)","journal-title":"IEEE Signal Proc. Lett."},{"key":"46_CR22","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., et al.: CosFace: Large Margin Cosine Loss for Deep Face Recognition (2018). arXiv:1801.0941"},{"issue":"10","key":"46_CR23","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multi-task cascaded convolutional networks. IEEE Signal Proc. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Proc. Lett."},{"key":"46_CR24","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: NIPS (2016)"},{"key":"46_CR25","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions, arXiv preprint (2016). arXiv:1610.02357"},{"key":"46_CR26","unstructured":"Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch, arXiv preprint (2014). arXiv:1411.7923"},{"key":"46_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: CVPR (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"46_CR28","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (2015)"},{"key":"46_CR29","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., et al.: Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference, arXiv preprint (2017). arXiv:1712.05877"},{"key":"46_CR30","unstructured":"NCNN: a high-performance neural network inference framework optimized for the mobile platform, Apr 20 2018. https:\/\/github.com\/Tencent\/ncnn"},{"key":"46_CR31","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., et al.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"46_CR32","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)","DOI":"10.5244\/C.29.41"},{"key":"46_CR33","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Computer Vision and Pattern Recognition, pp. 2892\u20132900 (2015)","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"46_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Wen","year":"2016","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499\u2013515. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31"},{"issue":"12","key":"46_CR35","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1109\/LSP.2017.2726105","volume":"24","author":"W Deng","year":"2017","unstructured":"Deng, W., Chen, B., Fang, Y., Hu, J.: Deep Correlation Feature Learning for Face Verification in the Wild. IEEE Signal Proc. Lett. 24(12), 1877\u20131881 (2017)","journal-title":"IEEE Signal Proc. Lett."},{"key":"46_CR36","doi-asserted-by":"crossref","unstructured":"Ng, H.W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: IEEE International Conference on Image Processing (ICIP), pp. 343\u2013347 (2014)","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"46_CR37","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding, CoRR, abs\/1510.00149 (2015)"},{"key":"46_CR38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-97909-0_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,28]],"date-time":"2022-08-28T22:24:59Z","timestamp":1661725499000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-97909-0_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319979083","9783319979090"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-97909-0_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}