A method to reduce the computational cost of Modified Hausdorff Distance in Face Recognition

2019 ◽  
Author(s):  
Bui Thanh Tinh ◽  
Truong Thien Nhan ◽  
Dang Nguyen Chau
Keyword(s):  
Face Recognition ◽  
Hausdorff Distance ◽  
Computational Cost ◽  
Modified Hausdorff Distance

scholarly journals A modification of Modified Hausdorff Distance method applying for face recognition

2017 ◽  
Vol 20 (K3) ◽  
pp. 126-131 ◽  
Author(s):  
Chau Nguyen Dang ◽  
Tuan Hong Do
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Hausdorff Distance ◽  
Computational Cost ◽  
Modern Life ◽  
Distance Method ◽  
Lighting Conditions ◽  
Modified Hausdorff Distance ◽  
Low Computational Cost ◽  
Face Expressions

Face recognition, that has a lot of applications in modern life, is still an attractive research for pattern recognition community. Due to the similarity of human face, face recognition presents a significant chalenge for pattern recognition researchers. Modified Hausdorff distance (MHD) is a low computational cost while giving high accuracy for face recognition. In this paper, a modification of MHD (MMHD) is proposed. By applying the ratio of high confident into the calculation of the distance between images, the MMHD gives higher accuracy in face recognition in comparing with MHD method. The MMHD method also gives higher performance than MHD method in face recognition in various non-ideal conditions of image: 1) varying lighting conditions, 2) varying face expressions and 3) varying of poses.

Hausdorff Distance for Face Recognition

2015 ◽  
pp. 39-46
Keyword(s):  
Face Recognition ◽  
Hausdorff Distance ◽  
Similarity Measures ◽  
Face Verification ◽  
3D Data ◽  
Modified Hausdorff Distance

When two sets are differently sized, the Hausdorff distance can be computed between them, even if the cardinality of one set is infinite. Different versions of this distance have been proposed and employed for face verification, among which the modified Hausdorff distance is the most famous. The important point to be noted is that, among the most commonly used similarity measures, the Hausdorff distance is the only one that has been widely applied to 3D data.

scholarly journals A modification of line Hausdorff distance for face recognition to reduce computational cost

2017 ◽  
Vol 20 (K3) ◽  
pp. 152-158 ◽  
Author(s):  
Chau Nguyen Dang ◽  
Tuan Hong Do
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Hausdorff Distance ◽  
Computational Cost ◽  
Ideal Condition ◽  
Modern Life ◽  
Significant Challenge ◽  
Face Expression ◽  
Lighting Conditions ◽  
Human Faces

Face recognition, that has a lot of applications in modern life, is still an attractive research for pattern recognition community. Due to the similarity of human faces, face recognition presents a significant challenge for pattern recognition researchers. Hausdorff distance is an efficient parameter for measuring the similarity between objects. Line Hausdorff distance (LHD) technique, which is the applying of Hausdorff distance for face recognition, gives high accuracy in comparing with common methods for face recognition. For fast screen techniques such as LHD, the computational cost is a key issue. A modified Line Hausdorff distance (MLHD) is proposed in this paper. The performance of the proposed method is compared with LHD method for face recognition in various conditions: 1) ideal condition of face, 2) varying lighting conditions, 3) varying poses and 4) varying face expression. It is very encouraging that the proposed method gives lower computational cost than LHD while keeping the accuracy of face recognition equal to the LHD method.

Binary Image Recognition Utilizing Computer Generated Templates

2017 ◽  
Author(s):  
Dustin Bielecki ◽  
Prakhar Jaiswal ◽  
Rahul Rai
Keyword(s):  
Image Recognition ◽  
Hausdorff Distance ◽  
Binary Image ◽  
Sorting Technique ◽  
Milling Operation ◽  
Modified Hausdorff Distance ◽  
Image Templates

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.

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