Matching 3D face scans using interest points and local histogram descriptors
نویسندگان
چکیده
منابع مشابه
Biometric identification using 3D face scans.
Biometrics is an emerging area of bioengineering that pursues the characterization of a person by means of something that the person is or produces. Face recognition is a particularly attractive biometric challenge. Most of the face recognition research performed in the past used 2D intensity images. However, algorithms based on 2D images are not robust to changes of illumination in the environ...
متن کاملFace recognition using 3D surface-extracted descriptors
The discriminating power of three dimensional (3D) descriptors extracted from 3D human face surfaces is analyzed. An automatic face recognition system using different subsets of the descriptor set has been implemented and tested. We used 420 3D-facial meshes belonging to 60 individuals, including views presenting light rotations and facial expressions, for the experiments. An HK segmentation (b...
متن کاملFace recognition using Weber local descriptors
This paper presents a method for face recognition using multi-scale Weber local descriptors (WLDs) and multi-level information fusion. Our method introduces the WLD, a novel and robust local descriptor, to describe the facial images and modifies it by a non-linear quantization approach to enhance its discriminative power. Moreover, a multi-scale framework for WLD extraction with multi-level inf...
متن کاملUsing Top-Points as Interest Points for Image Matching
We consider the use of so-called top-points for object retrieval. These points are based on scale-space and catastrophe theory, and are invariant under gray value scaling and offset as well as scale-Euclidean transformations. The differential properties and noise characteristics of these points are mathematically well understood. It is possible to retrieve the exact location of a top-point from...
متن کاملOn Matching Interest Regions Using Local Descriptors - Can an Information Theoretic Approach Help?
This paper shows that the common task of interest region matching using local descriptors can be improved using a new similarity measure. The similarity measure is motivated by the information theoretic image alignment that maximize mutual information between images. A property of the mutual information metric is that it does not only depend on how similar the signals are but also how complex t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers & Graphics
سال: 2013
ISSN: 0097-8493
DOI: 10.1016/j.cag.2013.04.001