research-article

Antifaces: A Novel, Fast Method for Image Detection

Authors:

Daniel Keren,

Margarita Osadchy,

Craig GotsmanAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 23, Issue 7

Pages 747 - 761

https://doi.org/10.1109/34.935848

Published: 01 July 2001 Publication History

Publisher Site

Abstract

This paper offers a novel detection method, which works well even in the case of a complicated image collection for instance, a frontal face under a large class of linear transformations. It is also successfully applied to detect 3D objects under different views. Call the collection of images, which should be detected, a multitemplate. The detection problem is solved by sequentially applying very simple filters (or detectors), which are designed to yield small results on the multitemplate (hence, antifaces ), and large results on random natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors are designed to act independently so that their false alarms are uncorrelated; this results in a false alarm rate which decreases exponentially in the number of detectors. This, in turn, leads to a very fast detection algorithm. Typically, $(1+\delta)N$ operations are required to classify an N-pixel image, where $\delta< 0.5$. Also, the algorithm requires no training loop. The algorithm's performance compares favorably to the well-known eigenface and support vector machine based algorithms, but is substantially faster.

References

[1]

S. Baker and S.K. Nayar, "Pattern Rejection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 544-549, 1996.

Digital Library

Google Scholar

[2]

P.N. Belhumeur, P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.

Digital Library

Google Scholar

[3]

J. Ben-Arie and K.R. Rao, "A Novel Approach for Template Matching by Nonorthogonal Image Expansion," IEEE Trans. Circuits and Systems for Video Technology, vol. 3, no. 1, pp. 71-84, 1993.

Digital Library

Google Scholar

[4]

M. Bichsel and A.P. Pentland, "Human Face Recognition and the Face Image Set's Topology," Proc. CVGIP: Image Understanding, vol. 59, no. 2, pp. 254-261, 1994.

Digital Library

Google Scholar

[5]

R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.

Digital Library

Google Scholar

[6]

S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, June 1984.

Digital Library

Google Scholar

[7]

R.C. Gonzalez and P.A. Wintz, Digital Image Processing. Addison-Wesley, 1992.

Digital Library

Google Scholar

[8]

D. Keren and M. Werman, "Probabilistic Analysis of Regularization," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 982-995, Oct. 1993.

Digital Library

Google Scholar

[9]

M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 103-108, 1990.

Digital Library

Google Scholar

[10]

Y. Lamdan and H.J. Wolfson, "Geometric Hashing: A General and Efficient Model-Based Recognition Scheme," Proc. Int'l. Conf. Computer Vision, pp. 238-249, 1988.

Google Scholar

[11]

C.H. Lo and H.S. Don, "3D Moment Forms: Their Construction and Application to Object Identification and Positioning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 1053-1064, 1989.

Digital Library

Google Scholar

[12]

B. Moghaddam and A. Pentland, "Probabilistic Visual Learning for Object Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, July 1997.

Digital Library

Google Scholar

[13]

H. Murase and S.K. Nayar, "Visual Learning and Recognition of 3D Objects from Appearance," Int'l J. Computer Vision, vol. 14, no. 1, pp. 5-24, Jan. 1995.

Digital Library

Google Scholar

[14]

E. Osuna, R. Freund, and F. Girosi, "Training Support Vector Machines: An Application to Face Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997.

Digital Library

Google Scholar

[15]

C.P. Papageorgiou, M. Oren, and T. Poggio, "A General Framework for Object Detection," Proc. Int'l Conf. Computer Vision, pp. 555-562, 1998.

Digital Library

Google Scholar

[16]

M. Pontil and A. Verri, "Support Vector Machines for 3D Object Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646, June 1998.

Digital Library

Google Scholar

[17]

W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical Recipes. Cambridge Univ. Press, 1986.

Google Scholar

[18]

K.R. Rao and J. Ben-Arie, "Multiple Template Matching Using the Expansion Filter," IEEE Trans. Video Technology, vol. 4, no. 5, pp. 490-504, 1994.

Digital Library

Google Scholar

[19]

K.R. Rao and J. Ben-Arie, "Nonorthogonal Image Expansion Related to Optimal Template Matching in Complex Images," Proc. CVGIP: Graphical Models and Image Processing, vol. 56, no. 2, pp. 149-160, 1994.

Digital Library

Google Scholar

[20]

D. Roobaert and M.M. Van Hulle, "View-Based 3D Object Recognition with Support Vector Machines," Proc. IEEE Int'l Workshop Neural Networks for Signal Processing, pp. 77-84, 1999.

Google Scholar

[21]

H.A. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998.

Digital Library

Google Scholar

[22]

H.A. Rowley, S. Baluja, and T. Kanade, "Rotation Invariant Neural Network-Based Face Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1998.

Digital Library

Google Scholar

[23]

A. Shashua, "On the Equivalence between the Support Vector Machine for Classification and Sparsified Fisher's Linear Discriminant," Neural Processing Letters, vol. 9, no. 2, pp. 129-139, 1999.

Digital Library

Google Scholar

[24]

L. Sirovich and M. Kirby, "Low-Dimensional Procedure for the Characterization of Human Faces," J. Optical Soc. Am. A, vol. 4, no. 3, pp. 519-524, 1987.

Crossref

Google Scholar

[25]

G. Stockham, T.M. Cannon, and R.B. Ingebresten, "Blind Deconvolution through Digital Signal Processing," Proc. IEEE vol. 63, pp. 678-692, 1975.

Crossref

Google Scholar

[26]

K.K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and: Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.

Digital Library

Google Scholar

[27]

D.L. Swets and J. Weng, "Using Discriminant Eigenfeatures for Retrieval," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836, Aug. 1996.

Digital Library

Google Scholar

[28]

D. Terzopoulos, "Regularization of Visual Problems Involving Discontinuities," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, no. 8, pp. 413-424, Aug. 1986.

Digital Library

Google Scholar

[29]

M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.

Digital Library

Google Scholar

[30]

M. Turk and A. Pentland, "Face Recognition Using Eigenfaces," Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.

Google Scholar

[31]

M. Turk, personal communication, Dec. 1999.

Google Scholar

[32]

M. Uenohara and T. Kanade, "Use of the Fourier and Karhunen-Loeve Decomposition for Fast Pattern Matching with a Large Set of Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 8, pp. 891-897, Aug. 1997.

Digital Library

Google Scholar

[33]

V.N. Vapnik, The Nature of Statistical Learning Theory. Berlin: Springer-Verlag, 1995.

Digital Library

Google Scholar

[34]

I. Weiss, "Geometric Invariants and Object Recognition," Int'l J. Computer Vision, vol. 10, no. 3, pp. 201-231, June 1993.

Digital Library

Google Scholar

Cited By

View all

Puhalanthi NLin D(2020)Effective multiple person recognition in random video sequences using a convolutional neural networkMultimedia Tools and Applications10.1007/s11042-019-7323-z79:15-16(11125-11141)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1007/s11042-019-7323-z
Das AKumar RKisku D(2016)Heterogeneous Face DetectionProceedings of the International Conference on Internet of things and Cloud Computing10.1145/2896387.2896417(1-6)Online publication date: 22-Mar-2016
https://dl.acm.org/doi/10.1145/2896387.2896417
Zafeiriou SZhang CZhang Z(2015)A survey on face detection in the wildComputer Vision and Image Understanding10.1016/j.cviu.2015.03.015138:C(1-24)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.cviu.2015.03.015
Show More Cited By

Index Terms

Antifaces: A Novel, Fast Method for Image Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

Recommendations

Refined Cattle Detection Using Composite Background Subtraction and Brightness Intensity from Bird's Eye Images
SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

Breeding cattle are known to be social animals that make groups as humans. Focusing on the sociality of the cattle, this paper aims to grasp and predict the conditions of breeding cattle by detecting the interactions between them. In order to detect ...
Recaptured images detection algorithm based on frequency domain transformation¹

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image ...
Traffic Sign Detection for Panoramic Images Using Convolution Neural Network Technique
HPCCT '19: Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

This research presents a method for panoramic traffic sign images detection for regulatory signs and guide signs especially blue and green sign. A new approach for detecting the signs inside a large panoramic image was considered. A convolution neural ...

Reviews

Reviewer: Brian Mayoh

Detection of images in natural pictures, such as faces, ships, and buildings, is an important and difficult problem. A new method of image detection introduced in this paper seems to be more accurate, more flexible, and much faster than previous methods. The collection of images to be detected (objects from various viewpoints, for example) is called the “multitemplate.” The method proposed in this paper solves the detection problem by sequentially applying very simple filters that are designed to yield large results on “random” natural pictures, and small results on the multitemplate (hence “antifaces”). The improved accuracy and flexibility of the method come from an empirically verified probabilistic assumption regarding the distribution of natural images. The improved speed arises from the simplicity of the filters, and from the fact that only images that pass a filter are sent to the next filter. The filters are designed to act independently, so false alarms are uncorrelated, and the false alarm rate decreases exponentially according to the number of filters. The method was tried in three very different multitemplates: face-finding, planar-object-finding and detection of the COIL 3D objects. Its performance compares favorably to the well-known eigenface and support vector methods, but it is substantially faster. In the near future, I expect to see an adaptation of this fast method for image detection in video. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 23, Issue 7

July 2001

94 pages

ISSN:0162-8828

Issue’s Table of Contents

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 July 2001

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Puhalanthi NLin D(2020)Effective multiple person recognition in random video sequences using a convolutional neural networkMultimedia Tools and Applications10.1007/s11042-019-7323-z79:15-16(11125-11141)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1007/s11042-019-7323-z
Das AKumar RKisku D(2016)Heterogeneous Face DetectionProceedings of the International Conference on Internet of things and Cloud Computing10.1145/2896387.2896417(1-6)Online publication date: 22-Mar-2016
https://dl.acm.org/doi/10.1145/2896387.2896417
Zafeiriou SZhang CZhang Z(2015)A survey on face detection in the wildComputer Vision and Image Understanding10.1016/j.cviu.2015.03.015138:C(1-24)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.cviu.2015.03.015
Mahmoodi MSayedi S(2015)A face detection method based on kernel probability mapComputers and Electrical Engineering10.1016/j.compeleceng.2015.02.00546:C(205-216)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.compeleceng.2015.02.005
Martínez-Zarzuela MDíaz-Pernas FAntón-Rodríguez MPerozo-Rondón FGonzález-Ortega D(2011)AdaBoost face detection on the gpu using Haar-like featuresProceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II10.5555/2009542.2009578(333-342)Online publication date: 30-May-2011
https://dl.acm.org/doi/10.5555/2009542.2009578
Brubaker SWu JSun JMullin MRehg J(2008)On the Design of Cascades of Boosted Ensembles for Face DetectionInternational Journal of Computer Vision10.1007/s11263-007-0060-177:1-3(65-86)Online publication date: 1-May-2008
https://dl.acm.org/doi/10.1007/s11263-007-0060-1
Osadchy MJacobs DLindenbaum M(2007)Surface Dependent Representations for Illumination Insensitive Image ComparisonIEEE Transactions on Pattern Analysis and Machine Intelligence10.5555/1191551.119178829:1(98-111)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.5555/1191551.1191788
Sahbi HGeman D(2006)A Hierarchy of Support Vector Machines for Pattern DetectionThe Journal of Machine Learning Research10.5555/1248547.12486227(2087-2123)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.5555/1248547.1248622
Hel-Or YHel-Or H(2005)Real-Time Pattern Matching Using Projection KernelsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2005.18427:9(1430-1445)Online publication date: 1-Sep-2005
https://dl.acm.org/doi/10.1109/TPAMI.2005.184
Rätsch MRomdhani STeschke GVetter T(2005)Over-complete wavelet approximation of a support vector machine for efficient classificationProceedings of the 27th DAGM conference on Pattern Recognition10.1007/11550518_44(351-360)Online publication date: 31-Aug-2005
https://dl.acm.org/doi/10.1007/11550518_44
Show More Cited By

Abstract

References

Cited By

Index Terms

Recommendations

Refined Cattle Detection Using Composite Background Subtraction and Brightness Intensity from Bird's Eye Images

Recaptured images detection algorithm based on frequency domain transformation1

Traffic Sign Detection for Panoramic Images Using Convolution Neural Network Technique

Reviews

Access critical reviews of Computing literature here

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations

Recaptured images detection algorithm based on frequency domain transformation¹