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.

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.

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 The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3327
Author(s):  
Vicente Román ◽  
Luis Payá ◽  
Adrián Peidró ◽  
Mónica Ballesta ◽  
Oscar Reinoso
Keyword(s):  
Mobile Robotics ◽  
A Priori ◽  
Computational Cost ◽  
Relevant Information ◽  
Local Features ◽  
New Family ◽  
Lighting Conditions ◽  
Omnidirectional Images ◽  
Great Development

Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.

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