Open Access Open Access  Restricted Access Subscription or Fee Access

Detection of Facial Parts based on ABLATA

Vikas Singh, Siddharth Choubey


Facial feature detection from standard 2D RGB images is a well-researched field but out of prolific techniques there isn't much 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.


Cite this Article:
Singh V, Choubey S. Detection of Facial Parts Based on ABLATA. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(1): 36–41p.


Face detection, ABLATA, feature selection.

Full Text:



Amberg T, Vetter B. Optimal landmark detection usingshape models and branch and bound. In: ICCV, 2011.

Belhumeur P, Jacobs D, Kriegman D, et al. Localizing parts of faces using a consensus of exemplars. In: CVPR, 2011.

Rapp V, Senechal T, Bailly K, et al. Multiple kernel learning svm and statistical validation for facial landmark detection. FG. 2011; 265–271p.

Roig G, Boix X, De la Torre F, et al. Hierarchical crf with product label spaces for parts-based models. FG. 2011; 657–664p.

Valstar M, Martinez B, Binefa X, et al. Facialpoint detection using boosted regression and graph models. CVPR. 2010; 2729–2736p.

Baker S, Matthews I. Lucas-kanade 20 years on: Aunifying framework. IJCV. 2004; 56(1): 221–255p.

Breiman L. Random forests. Machine Learning. 2001; 45(1): 5–32p.

Cootes T, Edwards G, Taylor C. Active appearance models. TPAMI. 2001; 23: 681–685p.

Cootes T, Taylor C. Active shape models-‘smartsnakes’. BMVC. 1992.

Fanelli G, Gall J, Van Gool L. Real time head poseestimation with random regression forests. CVPR. 2011.

Gall J, Yao A, Razavi N, et al. Hough forests for object detection, tracking, and action recognition. TPAMI . 2011.

Girshick R, Shotton J, Kohli P, et al. Efficient regression of general-activity human poses from depth images. ICCV. 2011.

Cootes T, Walker K, Taylor C. View-Based Active Appearance Models. Image and Vision Computing. 2002; 227–232p.

Gross R, Matthews I, Baker S. Generic Vs. Person specific active appearance models. Image and Vision Computing. 2005; 23: 1080–2093p.

Everingham M, Sivic J, Zisserman A. Hello! my name is... buffy-automatic naming of characters in TV video. BMVC. 2006.

Fanelli G, Weise T, Gall J, et al. Real timehead pose estimation from consumer depth cameras. DAGM. 2011.

Felzenszwalb P, Huttenlocher D. Pictorial structures for object recognition. IJCV. 2005; 61p.

Gall J, Lempitsky V. Class-specific hough forests forobject detection. CVPR. 2009; 1022–1029p.

Shotton J, Fitzgibbon A, Cook M, et al. Real-time human pose recognition in parts from single depth images. CVPR. 2011.

Sun M, Kohli P, Shotton J. Conditional Regression Forests for Human Pose Estimation. CVPR, 2012.

Viola P, Jones M. Robust real-time face detection. IJCV. 2004; 57(2): 137–154p.

Vukadinovic D, Pantic M. Fully automatic facial feature point detection using gabor feature based boosted classifiers. IEEE Int. Conf. on Systems, Man and Cybernetics. 2005; 1692–1698p.

Rai A. Attribute based level adaptive thresholding algorithm for object extraction. Journal of Advancement in Robotics. 2014; 1: 2p.

Rai A. 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. 2014; 3: 2p.


  • There are currently no refbacks.

This site has been shifted to