Elastic Bunch Graph Matching
نویسندگان
چکیده
منابع مشابه
Face Recognition by Elastic Bunch Graph Matching
We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth, etc.) are ...
متن کاملFace Recognition by Elastic Bunch Graph
We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system diiers from the preceding one 1] in three respects. Pha...
متن کامل2.5D Elastic graph matching
1077-3142/$ see front matter 2011 Elsevier Inc. A doi:10.1016/j.cviu.2010.12.008 ⇑ Corresponding author. E-mail addresses: [email protected] (S imperial.ac.uk (M. Petrou). In this paper, we propose novel elastic graph matching (EGM) algorithms for face recognition assisted by the availability of 3D facial geometry. More specifically, we conceptually extend the EGM algorithm in order to...
متن کاملSkin Segmentation based Elastic Bunch Graph Matching for efficient multiple Face Recognition
This paper is aimed at developing and combining different algorithms for face detection and face recognition to generate an efficient mechanism that can detect and recognize the facial regions of input image. For the detection of face from complex region, skin segmentation isolates the face-like regions in a complex image and following operations of morphology and template matching rejects fals...
متن کاملAverage Half Face Recognition by Elastic Bunch Graph Matching Based on Distance Measurement
Average-half-face experiments the overall accuracy of the system is better than using the original full face image. Clearly experiment shows that half face data produces higher recognition accuracy [5]. The average-half-face contain the data exactly half of the full face and thus results in storage and computational time saving. The information stored in average-half-face may be more discrimina...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2014
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.10587