3D face detection using transform invariant features

3D face detection using transform invariant features

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A generic, transform invariant 3D facial feature detection method based on mean (H) and Gaussian (K) curvature analysis is proposed. A scale space of the HK values is constructed differently from the previous HK attempts. The 3D features are extracted from this scale space and used in a global topology, which is trained with a Gaussian model using only faces with neutral and frontal poses. The model is then tested against 1323 faces with various poses and expressions. The method is compared with four other representative algorithms from the previous literature for 3D facial feature localisation and face detection purposes.


    1. 1)
      • Bowyer, K.W., Chang, K.I., Flynn, P.J.: `A survey of approaches to three-dimensional face recognition', Proc. 17th. IEEE Int. Conf. on Pattern Recognition, August 2004, Cambridge, UK, p. 358–361.
    2. 2)
      • K.I. Chang , K.W. Bowyer , P.J. Flynn . Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. , 10 , 1695 - 1700
    3. 3)
      • A. Colombo , C. Cusano , R. Schettini . 3D face detection using curvature analysis. J. Pattern Recognit. , 3 , 444 - 455
    4. 4)
      • Lu, X., Jain, A.K.: `Multimodal facial feature extraction for automatic 3D face recognition', Tech. Rep. MSU-CSE-05-22, August 2005.
    5. 5)
      • Lu, X., Jain, A.K.: `Automatic feature extraction for multiview 3D face recognition', Proc. 7th IEEE Int. Conf. on Automatic Face and Gesture Recognition, April 2006, Southampton, UK, p. 585–590.
    6. 6)
      • Akagündüz, E., Ulusoy, I.: `Scale and orientation invariant 3D interest point extraction using HK curvatures', Proc. IEEE 13th Int. Conf. Computer Vision, Workshop on 3D Representation for Recognition 3DRR, ICCV 2009, 2009, Kyoto, Japan.
    7. 7)
      • Savran, A.: `3D face recognition performance under adversorial conditions', in Proc. eNTERFACE'07 Workshop on Multimodal Interfaces, 2007, Istanbul, Turkey.

Related content

This is a required field
Please enter a valid email address