Face Recognition Technique Based on Active Appearance Model
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A face recognition technique is one of the common computer applications that automatically identifies or authenticates a person from a digital image or a video frame in a video source. This face recognition procedure is done by matching the selected face features from the image and the face database. Several face recognition techniques recognize the faces by extracting features from an image of the subject's face. The main challenges in face recognition techniques are to extract distinctive features that are not affected by external variations. Active Appearance Model (AAM) is one of the popular solutions that able to extract face features by precise modelling of human faces under various physical and environmental circumstances. In active appearance model approach, fitting the model with target image is a challenging task. The previous works shows that optimization approach is appropriate to solve this problem. Nevertheless, another problem is applying the optimization itself. In this paper, a face recognition technique based on AAM is proposed. In the proposed technique, the fitting problem of AAM is resolved by presenting ABC algorithm. The human face dataset called CASIA is utilized to analyse the performance of the proposed.
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M. Abul Kashem, N. Akhter, S. Ahmed, and M. Alam, Face Recognition System Based on Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN), Canadian Journal on Image Processing and Computer Vision, Vol. 2, No. 4, pp. 36-45, April 2011.
S. Kumar and S. Banerji, Face Recognition Using K2DSPCA ,In Proceedings of the International Conference on Information and Network Technology, Singapore, Vol. 4, pp. 84-88, 2011.
R. Ebrahimpour, A. Jahani, A. Amiri and M. Nazari, Expression-Independent Face Recognition Using Biologically Inspired Features, Indian Journal of Computer Science and Engineering, Vol. 2 No. 3, pp. 492-499, 2011.
A.K. Jain and A. Kumar, Biometrics of Next Generation: An Overview, Second Generation Biometrics, Springer, 2012.
Huang, Heisele and Blanz, Component-based Face Recognition with 3D Morphable Models , In Proceedings of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication, AVBPA, Guildford, UK, pp. 27-34, 2003.
H. Rady, Face Recognition using Principle Component Analysis with Different Distance Classifiers , JCSNS International Journal of Computer Science and Network Security, VOL.11, No.10, pp. 134-144, October 2011.
M. S. Ahuja and S. Chhabra, Effect Of Distance Measures In Pca Based Face Recognition, Vol. 1, No, 2, July 2011.
S. Kim, S. Chung, S. Jung, S. Jeon, J. Kim, and S. Cho, Robust Face Recognition using AAM and Gabor Features, World Academy of Science, Engineering and Technology, Vol. 33, pp. 117-121, 2007.
G.G. Gordon, Face Recognition Based on Depth and Curvature Features, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, IL , USA, pp. 808-810 , 1992.
H. Gao, H. K. Ekenel, M. Fischer and R. Stiefelhagen, Boosting Pseudo Census Transform Features for Face Alignment , In Proceedings of BMVC, Dundee, pp. 1-11, 2011.
P. Sauer, T. Cootes and C. Taylor, Accurate Regression Procedures for ActiveAppearance Models , BMVC, pp. 1-11, 2011.
D. Govindaraj, Application of Active Appearance Model to Automatic Face Replacement , Journal of Applied Statistics, 2011.
Patil, Kolhe and Patil, 2D Face Recognition Techniques: A Survey, International Journal of Machine Intelligence, Vol. 2, No. 1, pp-74-83, 2010.
T. Gernoth, K. A. Martinez, A. Gooßen and R. Grigat , Facial Pose Estimation using Active Appearance Models and a Generic Face Model, In Proceedings of the Fifth International Conference on Computer Vision Theory and Applications, Angers, France, Vol. 2, pp. 499-506, 2010.
A.Yuce, M. Sorci, and J. Thiran, Head Pose Detection Using Fast Robust PCA for Side Active Appearance Models Under Occlusion, In Proceedings of International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, USA, 2011.
T. Mosquera and Alba-Castro, Performance of active appearance model-basedpose-robust face recognition , The Institution of Engineering and Technology, Vol. 5, No. 6, pp. 348–357, 2011.
L. v. Maaten and E. Hendriks, Action unit classiﬁcation using active appearance models and conditional random ﬁelds, Cognitive Processing, 2011.
N. Stanarevic, Hybridizing artificial bee colony (ABC) algorithm with differential evolution for large scale optimization problems, International Journal of Mathematics and Computers in Simulation, Vol. 6, No. 1, pp. 194-202, 2012.
S. Kumar, T. Kumar Sharma, M. Pant and Ray, Adaptive Artificial Bee Colony for Segmentation of CT lung Images, In Proceedings of International Conference on Recent Advances and Future Trends in Information Technology, pp. 1-5, 2012.
Y. Zhang and L. Wu, Face Pose Estimation by Chaotic Artificial Bee Colony , International Journal of Digital Content Technology and its Applications, Vol. 5, No. 2, pp. 55-63, February 2011.
Z. Mustaffa and Y. Yusof, Optimizing LSSVM Using ABC For Non-Volatile Financial Prediction, Australian Journal of Basic and Applied Sciences, Vol. 5, No. 11, pp. 549-556, 2011.
D. Karaboga and B. Basturk, A powerful and efﬁcient algorithm for numerical function optimization: artiﬁcial bee colony (ABC) algorithm, Journal of Global Optimization, Vol. 39, pp. 459-471, 2007.
S. J. Lee , K. R. Park and J. Kim , A comparative study of facial appearance modeling methods for active appearance models, Pattern Recognition Letters, 30 , 1335–1346, 2009.
A.A. Mohammed , R.Minhas, Q.M.Jonathan Wu and M.A.Sid-Ahmed, Human face recognition based on multidimensional PCA and extreme learning machine , Pattern Recognition 44 , 2588–2597, 2011.
M. Balasubramanian ,S. Palanivel and V.Ramalingam, Fovea intensity comparison code for person identification and verification, Engineering Applications of Artificial Intelligence, 23 , 1277–1290, 2010.
M. Ma, J. Liang, M. Guo, Y.F. and Y. Yin, SAR image segmentation based on Artificial Bee Colony algorithm, Applied Soft Computing 11 , 5205–5214, 2011.
D. Karaboga and C. Ozturk, A novel clustering approach: Artificial Bee Colony (ABC) algorithm , Applied Soft Computing, 11 , 652–657, 2011.
BOLAJI, ASAJU LA'ARO, KHADER, A. TAJUDIN; AL-BETAR, M. AZMI; AWADALLAH, A. MOHAMMED. Artificial bee colony algorithm, its variants and applications : A survey , Journal of Theoretical and Applied Information Technology, Vol. 47 No.2, pp: 434-459, 2013.
D. Karaboga, B. Basturk, On the performance of artiﬁcial bee colony (ABC) algorithm, Applied Soft Computing, 8 (1), 687–697, 2008.
Chinese academy of sciences, CASIA face database V5, http://biometrics.idealtest.org/
Devi, A., Kavitha, A., Marimuthu, A., An improved self-updating face recognition authentication system, (2013) International Review on Computers and Software (IRECOS), 8 (7), pp. 1693-1701.
Luo, J., Ni, J., Xiao, Z., Zhang, H., 3D/4D face recognition: A comprehensive survey, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 35-42.
Shet, M.S., Patel, M., Manikantan, K., Ramachandran, S., DWT based feature extraction using Normalized Magnitude based Thresholding and Multilevel Illumination Normalization for enhanced Face Recognition, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 1969-1985.
Abhishek, A.K., Aneesh, M.U., Arun, B.V., Yaradoni, D.K.S., Manikantan, K., Ramachandran, S., Circular sector DCT based feature extraction for enhanced Face Recognition with image segmentation as a pre-processing step, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 1954-1968.
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