Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Scalable FPGA Accelerator for Convolutional Neural Networks Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

EffectFace: A Fast and Efficient Deep Neural Network Model for Face Recognition

verfasst von : Weicheng Li, Dan Jia, Jia Zhai, Jihong Cai, Han Zhang, Lianyi Zhang, Hailong Yang, Depei Qian, Rui Wang

Erschienen in: Advanced Computer Architecture

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Despite the Deep Neural Network (DNN) has achieved a great success in image recognition, the resource needed by DNN applications is still too much in terms of both memory usage and computing time, which makes it barely possible to deploy a whole DNN system on resource-limited devices such as smartphones and small embedded systems. In this paper, we present a DNN model named EffectFace designed for higher storage and computation efficiency without compromising the accuracy.
EffectFace includes two sub-modules, EffectDet for face detection and EffectApp for face recognition. In EffectDet we use sparse and small-scale convolution cores (filters) to reduce the number of weights for less memory usage. In EffectApp, we use pruning and weights-sharing technology to further reduce weights. At the output stage of the network, we use a new loss function rather than the traditional Softmax function to acquire feature vectors of the input face images, which reduces the dimension of the output of the network from n to fixed 128 where n equals to the number of categories to classify. Experiments show that, compared with previous models, the amounts of weights of our EffectFace is dramatically decreased (less than 10% of previous models) without losing the accuracy of recognition.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel DearDRAM: Discard Weak Rows for Reducing DRAM’s Refresh Overhead
Nächstes Kapitel A Power Efficient Hardware Implementation of the IF Neuron Model
Literatur
1.
Li, H., Lin, Z., Shen, X., et al.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
2.
Farfade, S.S., Saberian, M.J., Li, L.J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 643–650R. ACM (2015)
3.
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
4.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
5.
Mignon, A.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Computer Vision and Pattern Recognition, pp. 2666–2672. IEEE (2012)
6.
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings CVPR (2005)
7.
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 498–505. IEEE (2009)
8.
Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882 (2014)
9.
Huang, C., Zhu, S., Yu, K.: Large-scale strongly supervised ensemble metric learning: U.S. Patent 8,873,844[P], 28 October 2014
10.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
11.
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)
12.
Taigman, Y., Yang, M., Ranzato, M.A., et al.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
13.
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, 815–823 (2015)
14.
15.
Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber 56(4), 3–7 (2016)
16.
Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Advances in Neural Information Processing Systems (2015)
17.
18.
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2013)
19.
Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)CrossRef
20.
LeCun, Y., Boser, B., Denker, J.S., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)CrossRef
21.
LeCun, Y., Denker, J.S., Solla, S.A., et al.: Optimal brain damage. NIPs 2, 598–605 (1989)
22.
Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing Systems, p. 164 (1993)
23.
Han, S., Pool, J., Tran, J., et al.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp. 1135–1143 (2015)
24.
Weinberger, K., Dasgupta, A., Langford, J., et al.: Feature hashing for large scale multitask learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1113–1120. ACM (2009)
25.
Shi, Q., Petterson, J., Dror, G., et al.: Hash kernels for structured data. J. Mach. Learn. Res. 10(Nov), 2615–2637 (2009)
26.
Ganchev, K., Dredze, M.: Small statistical models by random feature mixing. In: Proceedings of the ACL08 HLT Workshop on Mobile Language Processing, pp. 19–20 (2008)
27.
Vaillant, R., Monrocq, C., Cun, Y.L.: Original approach for the localization of objects in images. IEE Proc. Vis. Image Sig. Process. 141(4), 245–250 (1994)CrossRef
Metadaten
Titel
EffectFace: A Fast and Efficient Deep Neural Network Model for Face Recognition
verfasst von
Weicheng Li
Dan Jia
Jia Zhai
Jihong Cai
Han Zhang
Lianyi Zhang
Hailong Yang
Depei Qian
Rui Wang
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Advanced Computer Architecture

Print ISBN: 978-981-13-2422-2

Electronic ISBN: 978-981-13-2423-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-2423-9_10

Neuer Inhalt

    Bildnachweise