DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION
Keywords:
haar Like Features, AdaBoost Algorithm, Cascade of Boosted Classifiers, DSIFT, Face recognition, Local Naive Bayes Nearest Neighbor
Abstract
Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.
Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for face
recognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.
Downloads
Download data is not yet available.
References
[1] Lowe D G. Distinctive image features from ScaleInvariant keypoints. International Journal of Computer Vision. 2004;60(2):91–110. Available from:
DOI: 10.1007/978-3-540-45243-0_39.
[2] Bosch A, Zisserman A, Munoz X. Image
classifcation using random forests and ferns.
In Proc ICCV. 2007;Available from: DOI
10.1109/ICCV.2007.4409066.
[3] McCann S, Lowe D G. Local Naive Bayes Nearest
Neighbor for Image Classification. Technical Report
TR-2011-11. 2011;University of British Columbia.
[4] Kirby M, Sirovich L. A Low Dimensional Procedure
for the Characterization of Human Faces. Journal of
the Optical Society of America A. 1988;4(3):519–524.
[5] Turk M A, Pentland A P. Face Recognition Using
Eigenfaces. IEEE. 1991;.
[6] Trần Phước Long, Nguyễn Văn Lượng. Nhận dạng
người dựa vào thông tin khuôn mặt xuất hiện trên
ảnh [Luận văn Cử nhân Tin học]. Trường Đại học
Khoa học Tự nhiên; 2003. Tr. 39–41, 47–62.
[7] Lu Boun Vinh, Hoàng Phương Anh. Nghiên cứu và
xây dựng hệ thống nhận dạng mặt người dựa trên
FSVM và AdaBoost [Luận văn Cử nhân Tin học].
Trường Đại học Khoa học Tự nhiên; 2011. Tr. 39–41,
47–62.
[8] Zuo W, Zhang D, Yang J, Wang K. BDPCA plus
LDA: a novel fast feature extraction technique for face
recognition. IEEE Transactions on Systems, Man, And
Cybernetics – Part B: Cybernetics. 2006;36(4).
[9] Chen Y, Yaou Zhao. Face Recognition Using DCT
and Hierarchical RBF Model. School of Information Science and Engineering Jinan University.
2006;Available from: DOI 10.1007/11875581_43.
[10] Aly M. Face Recognition Using SIFT Features.
Technical Report, Caltech; 2006.
[11] Chennamma H R, Lalitha Rangarajan, Veerabhadrappa. Face Identification from Manipulated Facial Images using SIFT. Department
of Studies in Computer Science University of
Mysore, Mysore, India. 2011;Available from: Doi
10.1109/ICETET.2010.33.
[12] Sharif M, Sajjad Mohsin, Muhammad Younas Javed,
Muhammad Atif Ali. Single Image Face Recognition
Using Laplacian of Gaussian and Discrete Cosine
Transforms. The International Arab Journal of Information Technology. 2012;9(6).
[13] Suhas S Satonkar, Kurhe Ajay B, Dr Prakash
Khanale B. Face Recognition Using Principal Component Analysis and Linear Discriminant Analysis on
Holistic Approach in Facial Images Database. IOSR
Journal of Engineering. 2012;2(12):15–22.
[14] Kumar H, Padmavati. Face Recognition using
SIFT by varying Distance Calculation Matching
Method. International Journal of Computer Application. 2012;47(3).
[15] Sunil, Renke Pradnya. Automatic Face Recognition
using Principal Component Analysis with DCT. Journal of Electronicsl and Communication Engineering
(IOSR-JECE). 2013;p. 01–07.
[16] Châu Ngân Khánh, Đoàn Thanh Nghị. Nhận dạng
mặt người với giải thuật Haar Like Feature – Cascade
of Boosted Classifiers và đặc trưng SIFT. Tạp chí
Khoa học Trường Đại học An Giang. 2014;3(2).
[17] Ladisla vLenc, Pavel Král. Automatic face recognition system based on the SIFT features. Computer
and Electrical Engineering. 2015;46.
[18] Ehsan Sadeghipour, Nasrollah Sahragard. Face
Recognition Based on Improved SIFT Algorithm.
International Journal of Advanced Computer Science
and Applications. 2016;7. Available from: DOI
10.1007/11875581_43.
[19] Lienhart R, Kuranov A, Pisarevsky V. Empirical
Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. MRL Technical
Report. 2002;Available from: DOI: 10.1007/978-3-
540-45243-0_39.
[20] Vedaldi A, Fulkerson B. Dense SIFT as a faster SIFT;
2017.
[21] Oren Boiman, Eli Shechtman, Michal Irani. In
Defense of Nearest-Neighbor Based Image Classification. In CVPR. 2008;.
[22] Mitchell T M. Machine Learning. McGraw-Hill, New
York, USA; 1997; tr. 128-153. Chapter 5.
[23] Bouzalmat A, Jamal Kharroubi, Arsalane Zarghili.
Face Recognition Using SVM Based on LDA. IJCSI
International Journal of Computer Science Issues.
2013;10(4).
DOI: 10.1007/978-3-540-45243-0_39.
[2] Bosch A, Zisserman A, Munoz X. Image
classifcation using random forests and ferns.
In Proc ICCV. 2007;Available from: DOI
10.1109/ICCV.2007.4409066.
[3] McCann S, Lowe D G. Local Naive Bayes Nearest
Neighbor for Image Classification. Technical Report
TR-2011-11. 2011;University of British Columbia.
[4] Kirby M, Sirovich L. A Low Dimensional Procedure
for the Characterization of Human Faces. Journal of
the Optical Society of America A. 1988;4(3):519–524.
[5] Turk M A, Pentland A P. Face Recognition Using
Eigenfaces. IEEE. 1991;.
[6] Trần Phước Long, Nguyễn Văn Lượng. Nhận dạng
người dựa vào thông tin khuôn mặt xuất hiện trên
ảnh [Luận văn Cử nhân Tin học]. Trường Đại học
Khoa học Tự nhiên; 2003. Tr. 39–41, 47–62.
[7] Lu Boun Vinh, Hoàng Phương Anh. Nghiên cứu và
xây dựng hệ thống nhận dạng mặt người dựa trên
FSVM và AdaBoost [Luận văn Cử nhân Tin học].
Trường Đại học Khoa học Tự nhiên; 2011. Tr. 39–41,
47–62.
[8] Zuo W, Zhang D, Yang J, Wang K. BDPCA plus
LDA: a novel fast feature extraction technique for face
recognition. IEEE Transactions on Systems, Man, And
Cybernetics – Part B: Cybernetics. 2006;36(4).
[9] Chen Y, Yaou Zhao. Face Recognition Using DCT
and Hierarchical RBF Model. School of Information Science and Engineering Jinan University.
2006;Available from: DOI 10.1007/11875581_43.
[10] Aly M. Face Recognition Using SIFT Features.
Technical Report, Caltech; 2006.
[11] Chennamma H R, Lalitha Rangarajan, Veerabhadrappa. Face Identification from Manipulated Facial Images using SIFT. Department
of Studies in Computer Science University of
Mysore, Mysore, India. 2011;Available from: Doi
10.1109/ICETET.2010.33.
[12] Sharif M, Sajjad Mohsin, Muhammad Younas Javed,
Muhammad Atif Ali. Single Image Face Recognition
Using Laplacian of Gaussian and Discrete Cosine
Transforms. The International Arab Journal of Information Technology. 2012;9(6).
[13] Suhas S Satonkar, Kurhe Ajay B, Dr Prakash
Khanale B. Face Recognition Using Principal Component Analysis and Linear Discriminant Analysis on
Holistic Approach in Facial Images Database. IOSR
Journal of Engineering. 2012;2(12):15–22.
[14] Kumar H, Padmavati. Face Recognition using
SIFT by varying Distance Calculation Matching
Method. International Journal of Computer Application. 2012;47(3).
[15] Sunil, Renke Pradnya. Automatic Face Recognition
using Principal Component Analysis with DCT. Journal of Electronicsl and Communication Engineering
(IOSR-JECE). 2013;p. 01–07.
[16] Châu Ngân Khánh, Đoàn Thanh Nghị. Nhận dạng
mặt người với giải thuật Haar Like Feature – Cascade
of Boosted Classifiers và đặc trưng SIFT. Tạp chí
Khoa học Trường Đại học An Giang. 2014;3(2).
[17] Ladisla vLenc, Pavel Král. Automatic face recognition system based on the SIFT features. Computer
and Electrical Engineering. 2015;46.
[18] Ehsan Sadeghipour, Nasrollah Sahragard. Face
Recognition Based on Improved SIFT Algorithm.
International Journal of Advanced Computer Science
and Applications. 2016;7. Available from: DOI
10.1007/11875581_43.
[19] Lienhart R, Kuranov A, Pisarevsky V. Empirical
Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. MRL Technical
Report. 2002;Available from: DOI: 10.1007/978-3-
540-45243-0_39.
[20] Vedaldi A, Fulkerson B. Dense SIFT as a faster SIFT;
2017.
[21] Oren Boiman, Eli Shechtman, Michal Irani. In
Defense of Nearest-Neighbor Based Image Classification. In CVPR. 2008;.
[22] Mitchell T M. Machine Learning. McGraw-Hill, New
York, USA; 1997; tr. 128-153. Chapter 5.
[23] Bouzalmat A, Jamal Kharroubi, Arsalane Zarghili.
Face Recognition Using SVM Based on LDA. IJCSI
International Journal of Computer Science Issues.
2013;10(4).
Published
01-December-2017
How to Cite
1.
Chau K, Doan N. DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION. journal [Internet]. 1Dec.2017 [cited 22Dec.2024];7(4):56-3. Available from: https://journal.tvu.edu.vn/tvujs_old/index.php/journal/article/view/46
Section
Articles
