science

Three-dimensional face recognition

Three-dimensional face recognition is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Three-dimensional face recognition rather than just read about it. In short: Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by meas…

Key takeaways

Three-dimensional face recognition belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Three-dimensional face recognition to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Three-dimensional face recognition from memory before moving on to harder problems.

Reference excerpt

Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation. Another approach is to use the 3D model to improve accuracy of traditional image based recognition by transforming the head into a known view. Additionally, most 3D scanners acquire both a 3D mesh and the corresponding texture. This allows combining the output of pure 3D matchers with the more traditional 2D face recognition algorithms, thus yielding better performance (as shown in FRVT 2006). The main technological limitation of 3D face recognition methods is the acquisition of 3D image, which usually requires a range camera. Alternatively, multiple images from different angles from a common camera (e.g. webcam) may be used to create the 3D model with significant post-processing. (See 3D data acquisition and object reconstruction.) This is also a reason why 3D face recognition methods have emerged significantly later (in the late 1980s) than 2D methods. Recently commercial solutions have implemented depth perception by projecting a grid onto the face and integrating video capture of it into a high resolution 3D model. This allows for good recognition accuracy with low cost off-the-shelf components. 3D face recognition is still an active research field, though several vendors offer commercial solutions.

See also 3D object recognition Facial recognition system

References

Okuwobi, I. P.; Chen, Q; Niu S.; et al. (2016). "Three-dimensional (3D) facial recognition and prediction". Signal, Image and Video Processing. 10 (6): 1151–1158. doi:10.1007/s11760-016-0871-z. S2CID 11211308. Bronstein, A. M.; Bronstein, M. M.; Kimmel, R. (2005). "Three-dimensional face recognition". International Journal of Computer Vision. 64 (1): 5–30. CiteSeerX 10.1.1.77.9592. doi:10.1007/s11263-005-1085-y. S2CID 670151. {{cite journal}}: Cite uses deprecated parameter |citeseerx= (help) Heseltine, T.; Pears, N.; Austin, J. (2008). "Three-dimensional face recognition using combinations of surface feature map subspace components". Image and Vision Computing. 26 (3): 382–396. doi:10.1016/j.imavis.2006.12.008. Kakadiaris, I. A.; Passalis, G.; Toderici, G.; Murtuza, N.; Karampatziakis, N.; Theoharis, T. (2007). "3D face recognition in the presence of facial expressions: an annotated deformable model approach". IEEE Transactions on Pattern Analysis and Machine Intelligence. 13 (12). Queirolo, C. C.; Silva, L.; Bellon, O. R.; Segundo, M. P. (2009). "3D Face Recognition using Simulated Annealing and the Surface Interpenetration Measure". IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (2): 206–19. doi:10.1109/TPAMI.2009.14. PMID 20075453. S2CID 12411479. Gupta, S.; Markey, M. K.; Bovik, A. C. (2010). "Anthropometric 3D Face Recognition". International Journal of Computer Vision. 90 (3): 331–349. doi:10.1007/s11263-010-0360-8. S2CID 10679755. A. Rashad, A Hamdy, M A Saleh and M Eladawy, "3D face recognition using 2DPCA", (IJCSNS) International Journal of Computer Science and Network Security, Vol.(12), 2009. http://paper.ijcsns.org/07_book/200912/20091222.pdf Spreeuwers, L.J. (2015). "Breaking the 99% barrier: optimisation of 3D face recognition". IET Biometrics. 4 (3): 169–177. doi:10.1049/iet-bmt.2014.0017. S2CID 195254. Spreeuwers, L.J. (2011). "Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers". International Journal of Computer Vision. 93 (3): 389–414. doi:10.1007/s11263-011-0426-2.

External links CVPR 2008 Workshop on 3D Face Processing Face Recognition Grand Challenge Face Recognition Homepage 3D Face Recognition Project and Research Papers Technion 3D face recognition project Mitsubishi Electric Research Laboratories 3D face recognition project Archived 2006-11-09 at the Wayback Machine L-1 Identity commercial 3D face recognition system Fast 3D scan technology for 3D face recognition at the Geometric Modelling and Pattern Recognition Group, UK 3D Face Recognition Using a Deformable Model at the Computational Biomedicine Lab, Houston, TX 3D Face Recognition Using Photometric Stereo, UK

Illustrations

Worked examples

Example 1 — a first encounter with Three-dimensional face recognition

Start with the simplest possible case. Write down what Three-dimensional face recognition claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Three-dimensional face recognition before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Three-dimensional face recognition ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Three-dimensional face recognition

In research: Three-dimensional face recognition appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Three-dimensional face recognition in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Three-dimensional face recognition is common in secondary-school and first-year university syllabi. It links to neighbouring topics 3D imaging, Facial recognition, so understanding it makes those chapters shorter.
In everyday life: Look for Three-dimensional face recognition outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Three-dimensional face recognition in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Three-dimensional face recognition means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Three-dimensional face recognition out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Three-dimensional face recognition in simple terms?

Why does Three-dimensional face recognition matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Three-dimensional face recognition?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Three-dimensional face recognition.