Note that, if you use class-based approach, you need to feed bounding box rectangle with values obtained from dlibfacedetection. If you use dlibfacelandmarkdetection, everything is already done for you (and you are using HOG face detection model). Face recognition (aka getting face descriptor). Dlib is a C toolkit containing machine learning algorithms and tools for creating complex software in C to solve real world problems.

Original author(s)Davis E. King
Initial release2002
Stable release
Written inC++
Operating systemCross-platform
TypeLibrary, machine learning

Dlib is a general purpose cross-platform software library written in the programming language C++. Its design is heavily influenced by ideas from design by contract and component-based software engineering. Thus it is, first and foremost, a set of independent software components. It is open-source software released under a Boost Software License.


Since development began in 2002, Dlib has grown to include a wide variety of tools. As of 2016, it contains software components for dealing with networking, threads, graphical user interfaces, data structures, linear algebra, machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and many other tasks. In recent years, much of the development has been focused on creating a broad set of statistical machine learning tools and in 2009 Dlib was published in the Journal of Machine Learning Research.[2] Since then it has been used in a wide range of domains.[3][4][5][6][7][8][9][10][11][12][13][14][15]

Dlib python

See also[edit]


  1. ^'Release 19.22'. 28 March 2021. Retrieved 10 April 2021.
  2. ^King, D. E. (2009). 'Dlib-ml: A Machine Learning Toolkit'(PDF). J. Mach. Learn. Res.10 (Jul): 1755–1758. CiteSeerX10.
  3. ^Scholarly research using Dlib
  4. ^Dlib on mloss.org
  5. ^Autonome Mobile Systeme 2009
  6. ^ESS: Extremely Simple Serialization for C++
  7. ^Gould, S. (2012). 'Darwin: A Framework for Machine Learning and Computer Vision Research and Development'(PDF). J. Mach. Learn. Res.13 (Dec): 3533–3537. CiteSeerX10.1.1.413.8518.
  8. ^Yan, Junchi, et al. 'Online incremental regression for electricity price prediction.' Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on. IEEE, 2012. Yan, J.; Tian, C.; Wang, Y.; Huang, J. (2012). 'Online incremental regression for electricity price prediction'. Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics. p. 31. doi:10.1109/SOLI.2012.6273500. ISBN978-1-4673-2401-4.
  9. ^Kuijf, Hugo J., Max A. Viergever, and Koen L. Vincken. 'Automatic Extraction of the Curved Midsagittal Brain Surface on MR Images.' Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. Springer Berlin Heidelberg, 2013. 225-232. Kuijf, H. J.; Viergever, M. A.; Vincken, K. L. (2013). 'Automatic Extraction of the Curved Midsagittal Brain Surface on MR Images'. Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. Lecture Notes in Computer Science. 7766. p. 225. doi:10.1007/978-3-642-36620-8_22. ISBN978-3-642-36619-2.
  10. ^Bormann, Richard Klaus Eduard. 'Vision-based place categorization.' (2010).
  11. ^Brodu, Nicolas, and Dimitri Lague. '3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology.' ISPRS Journal of Photogrammetry and Remote Sensing 68 (2012): 121–134.
  12. ^Aung, Zeyar, et al. 'Towards accurate electricity load forecasting in smart grids.' DBKDA 2012, The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications. 2012.
  13. ^Rodriguez, Alberto, et al. 'Abort and retry in grasping.' Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011. Rodriguez, A.; Mason, M. T.; Srinivasa, S. S.; Bernstein, M.; Zirbel, A. (2011). 'Abort and retry in grasping'. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. p. 1804. doi:10.1109/IROS.2011.6095100. ISBN978-1-61284-456-5.
  14. ^Mohan, Vandana, et al. 'Intraoperative prediction of tumor cell concentration from Mass Spectrometry Imaging.' Int. Symp. Math. Theo. Netw. Syst. 2010.
  15. ^Nakashima, Yuta, Noboru Babaguchi, and Jianping Fan. 'Detecting intended human objects in human-captured videos.' Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on. IEEE, 2010. Nakashima, Y.; Babaguchi, N.; Fan, J. (2010). 'Detecting intended human objects in human-captured videos'. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. p. 33. doi:10.1109/CVPRW.2010.5543721. ISBN978-1-4244-7029-7.

External links[edit]

  • Official website
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Dlib&oldid=994562050'




Cross Platform

iOS & Android & Windows10 UWP support.

Win & Mac & Linux Standalone support.

WebGL (asm.js/webassembly) support.

Lumin (Magic Leap) support.

Support for preview in the Editor.

Works with Unity Cloud Build.

ObjectDetection and ShapePrediction using Dlib C++ Library.

You can detect frontal human faces and face landmark(68 points) in Texture2D, WebCamTexture and Image byte array. In addition, You can detect a different objects by changing trained data file.

Include Many Examples


You will be able to develop applications using Augmented Reality, Virtual Reality and Mixed Reality technology.

Works with many hardware


Dlib Github

(e.g. HoloLens, Magic Leap One, Oculus Rift, Telepathy, Kinect, and Raspberry Pi).

Visual Scripting Support

Dlib Install

You can use All methods of DlibFaceLandmarkDetector in PlayMaker. (Using PlayMakerActions for DlibFaceLandmarkDetector)