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Detection of Facial Parts based on ABLATA

IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
© 2015 by IJCA Journal
ACEWRM 2015 - Number 2
Year of Publication: 2015
Siddhartha Choubey
Vikas Singh
Abha Choubey

Siddhartha Choubey, Vikas Singh and Abha Choubey. Article: Detection of Facial Parts based on ABLATA. IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering ACEWRM 2015(2):15-19, May 2015. Full text available. BibTeX

	author = {Siddhartha Choubey and Vikas Singh and Abha Choubey},
	title = {Article: Detection of Facial Parts based on ABLATA},
	journal = {IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering},
	year = {2015},
	volume = {ACEWRM 2015},
	number = {2},
	pages = {15-19},
	month = {May},
	note = {Full text available}


Facial feature detection from standard 2D RGB images is a well-researched field but out of prolific techniques there isn't amuch efficacy is achieved in the previous studies that can extract feature data even for a low quality images in real time. Hence, we propose an algorithm based on Attribute Based Level Adaptive Algorithm (ABLATA) which use recursive data estimates for this task. While the recursive data estimates learns the relation between patches of the localized segmented blocks and the location of nodes covering the region of the required regional properties of the face.


  • T. Amberg, B. Vetter. Optimal landmark detection usingshape models and branch and bound. In ICCV, 2011.
  • P. Belhumeur, D. Jacobs, D. Kriegman, and N. Kumar. Localizingparts of faces using a consensus of exemplars. InCVPR, 2011.
  • V. Rapp, T. Senechal, K. Bailly, and L. Prevost. Multiple kernellearning svm and statistical validation for facial landmarkdetection. In FG, pages 265–271, 2011.
  • G. Roig, X. Boix, F. De la Torre, J. Serrat, and C. Vilella. Hierarchicalcrf with product label spaces for parts-based models. In FG, pages 657–664, 2011.
  • M. Valstar, B. Martinez, X. Binefa, and M. Pantic. Facialpoint detection using boosted regression and graph models. In CVPR, pages 2729–2736, 2010.
  • S. Baker and I. Matthews. Lucas-kanade 20 years on: Aunifying framework. IJCV, 56(1):221 – 255, 2004.
  • L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
  • T. Cootes, G. Edwards, and C. Taylor. Active appearancemodels. TPAMI, 23:681–685, 2001.
  • T. Cootes and C. Taylor. Active shape models - 'smartsnakes'. In BMVC, 1992.
  • G. Fanelli, J. Gall, and L. Van Gool. Real time head poseestimation with random regression forests. In CVPR, 2011.
  • J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky. Hough forests for object detection, tracking, and actionrecognition. TPAMI, 2011.
  • R. Girshick, J. Shotton, P. Kohli, A. Criminisi, andA. Fitzgibbon. Efficient regression of general-activity humanposes from depth images. ICCV, 2011.
  • T. Cootes, K. Walker, and C. Taylor. View-Based ActiveAppearance Models. In Image and Vision Computing, pages227–232, 2002.
  • R. Gross, I. Matthews, and S. Baker. Generic vs. personspecific active appearance models. Image and Vision Computing,23:1080 – 2093, 2005.
  • M. Everingham, J. Sivic, and A. Zisserman. Hello! my nameis. . . buffy - automatic naming of characters in tv video. InBMVC, 2006.
  • Choubey ,S. and Kashyap , N. (2014) . A Survey on Various License Plate Detection Techniques from Vehicle Image. International Journal of Artificial Intelligence and Mechatronics . 2(6):171-174.
  • G. Fanelli, T. Weise, J. Gall, and L. Van Gool. Real timehead pose estimation from consumer depth cameras. DAGM,2011.
  • P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. IJCV, 61, 2005.
  • J. Gall and V. Lempitsky. Class-specific hough forests forobject detection. In CVPR, pages 1022–1029, 2009.
  • J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio,R. Moore, A. Kipman, and A. Blake. Real-time human poserecognition in parts from single depth images. In CVPR,2011.
  • M. Sun, P. Kohli, and J. Shotton. Conditional RegressionForests for Human Pose Estimation. In CVPR, 2012.
  • P. Viola and M. Jones. Robust real-time face detection. IJCV,57(2):137–154, 2004.
  • Sakshi Shrivastava and Siddhartha Choubey ,"A Comprehensive Survey for 3D Watermarking" , Research Journal Of Engineering & Technology (RJET-2011) Jan-Mar 2011, Raipur , Chhattisgarh Vol. 2 , issue 1 ,pp. 42-47 , ISSN 0976-2973 .
  • D. Vukadinovic and M. Pantic. Fully automatic facial featurepoint detection using gabor feature based boosted classifiers. In IEEE Int. Conf. on Systems, Man and Cybernetics, pages1692–1698, 2005.
  • Ankush Rai, Attribute Based Level Adaptive Thresholding Algorithm for Object Extraction, Journal of Advancement in Robotics, Volume 1, Issue 2.
  • Siddhartha Choubey , G. R. Sinha and Abha Choubey, "Bilateral Partitioning based character recognition for Vehicle License plate", International Conference on Advances in Information Technology and Mobile Communication – AIM 2011 April 21?22, 2011, Nagpur, Maharashtra, India,V. V. Das, G. Thomas, and F. Lumban Gaol (Eds. ): AIM 2011, CCIS 147, pp. 422–426, 2011, © Springer?Verlag Berlin Heidelberg 2011
  • Ankush Rai, Characterizing Face Encoding Mechanism by Selective Object Pattern in Brains using Synthetic Intelligence and Its Simultaneous Replication of Visual System That Encode Faces, Research & Reviews: Journal of Computational Biology, Volume 3, Issue 2.