Evaluation of Haar Cascade Classifiers Designed for Face Detection
In the past years a lot of effort has been made in the
field of face detection. The human face contains important features
that can be used by vision-based automated systems in order to
identify and recognize individuals. Face location, the primary step of
the vision-based automated systems, finds the face area in the input
image. An accurate location of the face is still a challenging task.
Viola-Jones framework has been widely used by researchers in order
to detect the location of faces and objects in a given image. Face
detection classifiers are shared by public communities, such as
OpenCV. An evaluation of these classifiers will help researchers to
choose the best classifier for their particular need. This work focuses
of the evaluation of face detection classifiers minding facial
landmarks.
[1] J. Fagertun, 2005. Face Recognition. Master Thesis, Technical
University of Denmark (DTU).
[2] Y. Gang, L. Jiawei, L. Jiayu, M. Qingli and Y. Ming, "Illumination
Variation in Face Recognition: A Review", IEEE Second International
Conference on Intelligent Networks and Intelligent Systems (ICINIS
2009), pp. 309-311.
[3] M.A. Turk, A.P. Pentland, "Face Recognition Using Eigenfaces",
Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR-91), 3-6 June 1991, Maui, Hawaii, USA, pp. 586-
591.
[4] H. Moon, P.J. Phillips, "Computational and Performance Aspects of
PCA-based Face Recognition Algorithms", Perception, Vol. 30, 2001,
pp. 303-321.
[5] K. Etemad, R. Chellappa, "Discriminant Analysis for Recognition of
Human Face Images", Journal of the Optical Society of America A, Vol.
14, No. 8, August 1997, pp. 1724-1733.
[6] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, "Face Recognition Using
LDA-Based Algorithms", IEEE Transaction on Neural Networks, Vol.
14, No. 1, January 2003, pp. 195-200.
[7] B. Heisele, P. Ho, T. Poggio, "Face Recognition with Support Vector
Machines: Global versus Component-based Approach", Proceedings. of
the Eighth IEEE International Conference on Computer Vision
(ICCV-01), Vol. 2, 09-12 July 2001, Vancouver, Canada, pp. 688-694.
[8] A. Lanitis, C.J. Taylor, T.F. Cootes, "Automatic Interpretation and
Coding of Face Images Using Flexible Models", IEEE Transaction
Pattern Analysis and Machine Intelligence (1997), pp. 743-756.
[9] E. Gomathi, K. Baskaran, "Recognition of Faces Using Improved
Principal Component Analysis", Second International Conference on
Machine Learning and Computing (ICMLC-10), pp.198-201.
[10] C. Liu and H. Wechsler, "Probabilistic Reasoning Models for Face
Recognition", in. IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR-98), pp.827-832.
[11] Y. Ji, T. Lin, and H. Zha, "Mahalanobis Distance Based Non-negative
Sparse Representation for Face Recognition", in Proceedings The
Eighth International Conference on Machine Learning and
Applications (ICMLA-09), pp.41-46.
[12] V. Kabeer & N. K. Narayanan, "Face recognition using state space
parameter and Artificial Neural Network Classifier", Proceedings of
IEEE International Conference on Computational Intelligence and
Multimedia Applications (ICCIMA-07), Sivakasi, India Vol.3,
December, 2007, pp 250-254.
[13] M. Castrill├│n, O. Déniz, D. Hern├índez, and J. Lorenzo. "A Comparison
of Face and Facial Feature Detectors based on the Viola-Jones General
Object Detection Framework". Machine Vision and Applications, vol.
22 issue 3, 2011.
[14] P. N. Bellhumer, J. Hespanha, and D. Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, Special
Issue on Face Recognition, 1997, pp. 711-720.
[15] C. Thomaz and G. Giraldi, "A new ranking method for principal
components analysis and its application to face image analysis", Journal
Image and Vision Computing, 2010, vol. 28, no. 6, pp. 902-913.
[16] P. Viola and M. Jones, "Robust real-time object detection,"
International Journal of Computer Vision, 2002 vol. 57, no. 2, pp. 137-
154.
[17] Inte, Intel Open Source Computer Vision Library, v1. 1ore,
http://sourceforge.net/projects/opencvlibrary (October 2011).
[18] Lienhart, R., Kuranov, A., Pisarevsky, V., "Empirical Analysis of
Detection Cascades of Boosted Classifiers for Rapid Object Detection",
in 25th Pattern Recognition Symposium (DAGM-03), pp. 297-304.
[19] Lienhart, R., Liang, L., Kuranov, A., "A Detector Tree of Boosted
Classifiers for Real-Time Object Detection and Tracking", in IEEE
International Conference on Multimedia and Expo (ICME-03), pp.
277-280.
[20] Wilson, P. I., Fernandez, J., "Facial feature detection using haar
classifiers", in Journal of Computing Sciences in Colleges (2006), pp.
127-133.
[21] Bradley, D. "Profile face detection" http://www.cs.cmu.edu/
~dbradley/publications/bradley-iurac-03.swf, (2003) last accessed
12/10/2011.
[22] Kruppa, H., Castrill├│n Santana, M., Schiele, B., "Fast and Robust Face
Finding via Local Context", in IEEE International Workshop on Visual
Surveillance and Performance Evaluation of Tracking and Surveillance
(VS-PETS-03), pp. 157-164.
[23] G. M. Beumer, Q. Tao, A. M. Bazen and R. N. J. Veldhuis, "A
Landmark Paper in Face Recognition". IEEE International Conference
on Automatic Face and Gesture Recognition (FGE-02), pp. 73-78.
[24] M. Koestinger, P. Wohlhart, P. M. Roth and H. Bischof, "Annotated
Facial Landmarks in the Wild: A Large-scale, Real-world Database for
Facial Landmark Localization", IEEE International Workshop on
Benchmarking Facial Image Analysis Technologies (BeFIT-11).
[25] M. Jian-Wei and F. Yu-Hua, "Face segmentation algorithm based on
ASM", IEEE Conference on Intelligent Computing and Intelligent
Systems (ICIS-09), 2009, pp 495-499.
[26] Z. Liu, W. Li, X. Zhang and J. Yang, "Efficient Face Segmentation
Based on Face Attention Model and Seeded Region Merging", 9th
International Conference on Signal Processing (ICSP-08). pp. 1116-
1119.
[27] A.M. Martinez and R. Benavente, "The AR Face Database", CVC
Technical Report #24, June - 1998.
[1] J. Fagertun, 2005. Face Recognition. Master Thesis, Technical
University of Denmark (DTU).
[2] Y. Gang, L. Jiawei, L. Jiayu, M. Qingli and Y. Ming, "Illumination
Variation in Face Recognition: A Review", IEEE Second International
Conference on Intelligent Networks and Intelligent Systems (ICINIS
2009), pp. 309-311.
[3] M.A. Turk, A.P. Pentland, "Face Recognition Using Eigenfaces",
Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR-91), 3-6 June 1991, Maui, Hawaii, USA, pp. 586-
591.
[4] H. Moon, P.J. Phillips, "Computational and Performance Aspects of
PCA-based Face Recognition Algorithms", Perception, Vol. 30, 2001,
pp. 303-321.
[5] K. Etemad, R. Chellappa, "Discriminant Analysis for Recognition of
Human Face Images", Journal of the Optical Society of America A, Vol.
14, No. 8, August 1997, pp. 1724-1733.
[6] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, "Face Recognition Using
LDA-Based Algorithms", IEEE Transaction on Neural Networks, Vol.
14, No. 1, January 2003, pp. 195-200.
[7] B. Heisele, P. Ho, T. Poggio, "Face Recognition with Support Vector
Machines: Global versus Component-based Approach", Proceedings. of
the Eighth IEEE International Conference on Computer Vision
(ICCV-01), Vol. 2, 09-12 July 2001, Vancouver, Canada, pp. 688-694.
[8] A. Lanitis, C.J. Taylor, T.F. Cootes, "Automatic Interpretation and
Coding of Face Images Using Flexible Models", IEEE Transaction
Pattern Analysis and Machine Intelligence (1997), pp. 743-756.
[9] E. Gomathi, K. Baskaran, "Recognition of Faces Using Improved
Principal Component Analysis", Second International Conference on
Machine Learning and Computing (ICMLC-10), pp.198-201.
[10] C. Liu and H. Wechsler, "Probabilistic Reasoning Models for Face
Recognition", in. IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR-98), pp.827-832.
[11] Y. Ji, T. Lin, and H. Zha, "Mahalanobis Distance Based Non-negative
Sparse Representation for Face Recognition", in Proceedings The
Eighth International Conference on Machine Learning and
Applications (ICMLA-09), pp.41-46.
[12] V. Kabeer & N. K. Narayanan, "Face recognition using state space
parameter and Artificial Neural Network Classifier", Proceedings of
IEEE International Conference on Computational Intelligence and
Multimedia Applications (ICCIMA-07), Sivakasi, India Vol.3,
December, 2007, pp 250-254.
[13] M. Castrill├│n, O. Déniz, D. Hern├índez, and J. Lorenzo. "A Comparison
of Face and Facial Feature Detectors based on the Viola-Jones General
Object Detection Framework". Machine Vision and Applications, vol.
22 issue 3, 2011.
[14] P. N. Bellhumer, J. Hespanha, and D. Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, Special
Issue on Face Recognition, 1997, pp. 711-720.
[15] C. Thomaz and G. Giraldi, "A new ranking method for principal
components analysis and its application to face image analysis", Journal
Image and Vision Computing, 2010, vol. 28, no. 6, pp. 902-913.
[16] P. Viola and M. Jones, "Robust real-time object detection,"
International Journal of Computer Vision, 2002 vol. 57, no. 2, pp. 137-
154.
[17] Inte, Intel Open Source Computer Vision Library, v1. 1ore,
http://sourceforge.net/projects/opencvlibrary (October 2011).
[18] Lienhart, R., Kuranov, A., Pisarevsky, V., "Empirical Analysis of
Detection Cascades of Boosted Classifiers for Rapid Object Detection",
in 25th Pattern Recognition Symposium (DAGM-03), pp. 297-304.
[19] Lienhart, R., Liang, L., Kuranov, A., "A Detector Tree of Boosted
Classifiers for Real-Time Object Detection and Tracking", in IEEE
International Conference on Multimedia and Expo (ICME-03), pp.
277-280.
[20] Wilson, P. I., Fernandez, J., "Facial feature detection using haar
classifiers", in Journal of Computing Sciences in Colleges (2006), pp.
127-133.
[21] Bradley, D. "Profile face detection" http://www.cs.cmu.edu/
~dbradley/publications/bradley-iurac-03.swf, (2003) last accessed
12/10/2011.
[22] Kruppa, H., Castrill├│n Santana, M., Schiele, B., "Fast and Robust Face
Finding via Local Context", in IEEE International Workshop on Visual
Surveillance and Performance Evaluation of Tracking and Surveillance
(VS-PETS-03), pp. 157-164.
[23] G. M. Beumer, Q. Tao, A. M. Bazen and R. N. J. Veldhuis, "A
Landmark Paper in Face Recognition". IEEE International Conference
on Automatic Face and Gesture Recognition (FGE-02), pp. 73-78.
[24] M. Koestinger, P. Wohlhart, P. M. Roth and H. Bischof, "Annotated
Facial Landmarks in the Wild: A Large-scale, Real-world Database for
Facial Landmark Localization", IEEE International Workshop on
Benchmarking Facial Image Analysis Technologies (BeFIT-11).
[25] M. Jian-Wei and F. Yu-Hua, "Face segmentation algorithm based on
ASM", IEEE Conference on Intelligent Computing and Intelligent
Systems (ICIS-09), 2009, pp 495-499.
[26] Z. Liu, W. Li, X. Zhang and J. Yang, "Efficient Face Segmentation
Based on Face Attention Model and Seeded Region Merging", 9th
International Conference on Signal Processing (ICSP-08). pp. 1116-
1119.
[27] A.M. Martinez and R. Benavente, "The AR Face Database", CVC
Technical Report #24, June - 1998.
@article{"International Journal of Information, Control and Computer Sciences:52036", author = "R. Padilla and C. F. F. Costa Filho and M. G. F. Costa", title = "Evaluation of Haar Cascade Classifiers Designed for Face Detection", abstract = "In the past years a lot of effort has been made in the
field of face detection. The human face contains important features
that can be used by vision-based automated systems in order to
identify and recognize individuals. Face location, the primary step of
the vision-based automated systems, finds the face area in the input
image. An accurate location of the face is still a challenging task.
Viola-Jones framework has been widely used by researchers in order
to detect the location of faces and objects in a given image. Face
detection classifiers are shared by public communities, such as
OpenCV. An evaluation of these classifiers will help researchers to
choose the best classifier for their particular need. This work focuses
of the evaluation of face detection classifiers minding facial
landmarks.", keywords = "Face datasets, face detection, facial landmarking,
haar wavelets, Viola-Jones detectors.", volume = "6", number = "4", pages = "434-4", }
