Skip to main content
SpringerLink
Log in

Consumer Imaging II: Faces, Portraits, and Digital Beauty

  • Reference work entry
  • First Online:
Handbook of Visual Display Technology

  • 297 Accesses

Abstract

In this second article, we explore some of these most recent technologies to make their way into today’s digital imaging devices. Photography is primarily about people and most of our photographs feature our family and friends. Here we explain how today’s cameras can detect and track faces and even facial features in real time. We look at some of the ways that the growing computational power available in cameras can help analyze, evaluate, and enhance images based on information derived from the faces in a scene. We’ll also take a look at how sophisticated eye tracking and analysis are now feasible and an overview of the classic red-eye defects that occur when flash photography is used and how this became the first computational imaging solution to reach the mass market. Finally, we review the implementation of a range of subtler and more sophisticated enhancements that can be applied to improve our portrait images and enhance our personal appearance in both photographs and video clips.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,399.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Having made this point, it is worth remarking that the latest handheld smartphones and tablets feature multi-core processors and GPU technology that are rapidly catching up on desktop capabilities. We live in interesting times.

  2. 2.

    You’ll find more details in Part I of this article.

  3. 3.

    In many texts these are written as I(A), I(B), etc., but let’s keep the notation simple here.

  4. 4.

    This assumes a software implementation of the algorithm; if you have a hardware tracker available, it can be implemented on larger screen sizes and scan a wider range of face scales.

  5. 5.

    Probably more than you would want to know!

Abbreviations

AAM:

Active appearance model

CCD:

Couple-charged device

ISP:

Image signal processor

IPP:

Image processing pipeline

RIP:

In-plane rotation

ROP:

Out-of-plane rotation

TMM:

Template-matching module

WQVGA:

Wide quarter video graphics array

WFOV:

Wide field of view

Further Reading

  • Bacivarov I (2009) Advances in the modelling of facial sub-regions and facial expressions using active appearance techniques. Doctoral dissertation

    Google Scholar 

  • Bacivarov I, Ionita M, Corcoran P (2008a) Statistical models of appearance for eye tracking and eye-blink detection and measurement. IEEE Trans Consum Electron 54(3):1312–1320

    Google Scholar 

  • Bacivarov I, Corcoran PM, Ionita MC (2008b) A statistical modeling based system for blink detection in digital cameras. In: 2008 digest of technical papers – international conference on consumer electronics, pp 1–2

    Google Scholar 

  • Bigioi P, Zaharia C, Corcoran P (2012) Advanced hardware real time face detector. IEEE international conference on consumer electronics (ICCE)

    Google Scholar 

  • Brubaker SC, Wu J, Sun J, Mullin MD, Rehg JM (2007) On the design of cascades of boosted ensembles for face detection. Int J Comput Vis 77(1–3):65–86

    Google Scholar 

  • Chang H, Haizhou A, Yuan L, Shihong L (2005) Vector boosting for rotation invariant multi-view face detection. In: Tenth IEEE international conference on computer vision (ICCV’05), vol 1, pp 446–453

    Google Scholar 

  • Corcoran P (2015) To gaze with undimmed eyes on all darkness [IP Corner]. IEEE Consum Electron Mag 4(1):99–103

    Article  Google Scholar 

  • Corcoran P, Bigioi P, Steinberg E, Pososin A (2005) Automated in-camera detection of flash-eye defects. IEEE Trans Consum Electron 51(1):11–17

    Article  Google Scholar 

  • Corcoran P, Steinberg E, Petrescu S, Drimbarean A, Nanu F, Pososin A, Biglol P (2008) U.S. Patent 7,315,631. U.S. Patent and Trademark Office, Washington DC

    Google Scholar 

  • Corcoran P, Steinberg E, Petrescu S, Drimbarean A, Nanu F, Pososin A, Bigioi P (2008) Real-time face tracking in a digital image acquisition device. 7469055 02 Dec 2008

    Google Scholar 

  • Corcoran PM, Nanu F, Petrescu S, Bigioi P (2012a) Real-time eye gaze tracking for gaming design and consumer electronics systems. IEEE Trans Consum Electron 58:347–355

    Article  Google Scholar 

  • Corcoran P, Bigioi P, Nanu F (2012b) Advances in the detection & repair of flash-eye defects in digital images-a review of recent patents. Recent Patents Electr Electron Eng 5(1):30–54

    Article  Google Scholar 

  • Corcoran PM, Bigioi P, Nanu F (2013) Half-face detector for enhanced performance of flash-eye filter. In: 2013 I.E. international conference on consumer electronics (ICCE), pp 252–253

    Google Scholar 

  • Corcoran P, Bigioi P, Nanu F (2014a) Detection and repair of flash-eye in handheld devices, Electron (ICCE), 2014 IEEE

    Google Scholar 

  • Corcoran P, Stan C, Florea C, Ciuc M, Bigioi P (2014b) Digital beauty: the good, the bad, and the (not-so) ugly. IEEE Consum Electron Mag 3(4):55–62

    Article  Google Scholar 

  • Do TT, Doan KN, Le TH, Le BH (2009) Boosted of Haar-like features and local binary pattern based face detection. In: 2009 IEEE-RIVF international conference on computing and communication technologies, pp 1–8

    Google Scholar 

  • Duchowski AT (2009) A breadth-first survey of eye-tracking applications. Behav Res Methods Instrum Comput 34(4):455–470

    Article  Google Scholar 

  • Gallagher P (2012) Smart-Phones get even smarter cameras [future visions]. IEEE Consum Electron Mag 1(1):25–30

    Google Scholar 

  • Hefenbrock D, Oberg J, Thanh NTN, Kastner R, Baden SB (2010) Accelerating Viola-Jones face detection to FPGA-level using GPUs. In: 2010 18th IEEE annual international symposium on field-programmable custom computing machines, pp 11–18

    Google Scholar 

  • Hiromoto M, Sugano H, Miyamoto R (2009) Partially parallel architecture for AdaBoost-based detection with Haar-like features. IEEE Trans Circuits Syst Video Technol 19(1):41–52

    Article  Google Scholar 

  • Huang C, Ai H, Li Y, Lao S (2007) High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4):671–686

    Article  Google Scholar 

  • Hutchinson TE, White KP, Martin WN, Reichert KC, Frey LA (1989) Human-computer interaction using eye-gaze input. IEEE Trans Syst Man Cybern 19(6):1527–1534

    Article  Google Scholar 

  • Ianculescu M, Bigioi P, Gangea M, Petrescu S, Corcoran P, Steinberg (2008) Real-time face tracking in a digital image acquisition device. 7403643 22 Jul 2008

    Google Scholar 

  • Jacob RJK (1991) The use of eye movements in human-computer interaction techniques: what you look at is what you get. ACM Trans Inf Syst 9(2):152–169

    Article  Google Scholar 

  • Kublbeck C, Ernst A (2006) Face detection and tracking in video sequences using the modified census transformation. Image Vis Comput 24(6):564–572

    Article  Google Scholar 

  • Lienhart R, Maydt J (2002) An extended set of Haar-like features for rapid object detection. In: Image processing, 2002 international conference on. proceedings, vol 1, pp 900–903

    Google Scholar 

  • Lui YM, Beveridge JR, Whitley LD (2010) Adaptive appearance model and condensation algorithm for robust face tracking. IEEE Trans Syst Man Cybern Part A Syst Humans 40(3):437–448

    Article  Google Scholar 

  • Messom C, Barczak A (2006) Fast and efficient rotated Haar-like features using rotated integral images. In: Australasian conference on robotics and automation, pp 1–6

    Google Scholar 

  • Nanu F, Petrescu S, Corcoran P, Bigioi P (2011) Face and gaze tracking as input methods for gaming design. In: International games innovation, IEEE conference on, IGIC 2011, pp 115–116

    Google Scholar 

  • Papageorgiou CP, Oren M, Poggio T (1998) A general framework for object detection. In: Computer vision, 1998. Sixth international conference on, pp 555–562

    Google Scholar 

  • Ren J, Kehtarnavaz N, Estevez L (2008) Real-time optimization of Viola -Jones face detection for mobile platforms. In: 2008 I.E. Dallas circuits and systems workshop: system-on-chip – design, applications, integration, and software, pp 1–4

    Google Scholar 

  • Ren H, Che M, Haiwang R, Ming C (2011) A multi-core architecture for face detection based on application specific instruction-set processor. In: Multimedia technology (ICMT), 2011 international conference on, vol 10, pp 3354–3357

    Google Scholar 

  • Steinberg S. (2005) Method and apparatus for the automatic real-time detection and correction of red-eye defects in batches of digital images or in handheld appliances. US patent 6,873,743

    Google Scholar 

  • Tresadern PA, Ionita MC, Cootes TF (2012) Real-time facial feature tracking on a mobile device. Int J Comput 96(3):280–289

    Google Scholar 

  • Verschae R, Ruiz-del-Solar J, Correa M (2007) A unified learning framework for object detection and classification using nested cascades of boosted classifiers. Mach Vis Appl 19(2):85–103

    Article  Google Scholar 

  • Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proceedings 2001 I.E. Computer Society conference on computer vision and pattern recognition (CVPR), vol 1; IEEE Comput Soc 511–518

    Google Scholar 

  • Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  • Wang P, Shen C, Zheng H, Ren Z (2010) Training a multi-exit cascade with linear asymmetric classification for efficient object detection. In: 2010 I.E. international conference on image processing, pp 61–64

    Google Scholar 

  • Whitehill J, Littlewort G, Fasel I, Bartlett M, Movellan J (2009) Toward practical smile detection. IEEE Trans Pattern Anal Mach Intell 31:2106–2111

    Article  Google Scholar 

  • Wu B, Ai H, Huang C, Lao S (2004) Fast rotation invariant multi-view face detection based on real adaboost. In: Sixth IEEE international conference on automatic face and gesture recognition, 2004. Proceedings, pp 79–84

    Google Scholar 

  • Zaharia C, Bigioi P, Corcoran P (2011) Hybrid video-frame pre-processing architecture for HD-video. In: IEEE international conference on consumer electronics (ICCE), pp 89–90

    Google Scholar 

  • Zeng H, Luo R (2013) Colour and tolerance of preferred skin colours on digital photographic images. Color Res Appl 38:30–45

    Article  Google Scholar 

Useful URL

Books

  • Corcoran P (ed) New approaches to characterization and recognition of faces. InTech. 262 p, Chapters published August 01, 2011 under CC BY-NC-SA 3.0 license. doi: 10.5772/994

    Google Scholar 

  • Corcoran PM (ed) Reviews, refinements and new ideas in face recognition. InTech. 338 p, Chapters published July 27, 2011 under CC BY-NC-SA 3.0 license. doi:10.5772/743

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Engineering and Informatics, National University of Ireland, Galway, Ireland

    Peter Corcoran

  2. FotoNation Ltd., Galway, Ireland

    Petronel Bigioi

Authors
  1. Peter Corcoran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petronel Bigioi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Corcoran .

Editor information

Editors and Affiliations

  1. Display Technology Center, Panel Integration Division I, Industrial Technology Research Institute (ITRI), Chutung, Hsinchu, Taiwan

    Janglin Chen

  2. Sheffield Hallam University, Sheffield, United Kingdom

    Wayne Cranton

  3. Veritas et Visus, Temple, Texas, USA

    Mark Fihn

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this entry

Cite this entry

Corcoran, P., Bigioi, P. (2016). Consumer Imaging II: Faces, Portraits, and Digital Beauty. In: Chen, J., Cranton, W., Fihn, M. (eds) Handbook of Visual Display Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-14346-0_210

Download citation

Publish with us

Policies and ethics

Search

Navigation