Table 2 from Face Recognition by Elastic Bunch Graph Matching. Abstract. 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 described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straightforward comparison of image graphs.
We report recognition experiments on the FERET database as well as the Bochum database, including recognition across pose. Extracted Key Phrases.
- Face Recognition by Elastic Bunch Graph Matching* Laurenz Wiskott it, Jean-Marc Fellous 2~, Norbert Kriiger 1, and Christoph vonder Malsburg 1'2 i Institut fiir Neuroinformatik Ruhr-Universit~t Bochum D-44780.
- 90 2013 13th International Conference on Hybrid Intelligent Systems (HIS) Figure 2. Result of standardization geometric face We used for the alignment phase coordinates of eyes the algorithm proposed by .
- 776 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.
- Gabor Based Face Recognition Using EBGM and PCA. Face Bunch Graph (FBG) .
- Hence, implementing the technique of Elastic Bunch Graph matching (EBGM) after skin segmentation generates Face Bunch Graphs. Template Matching, Face Bunch Graph, Face Recognition Database of University of Essex 1 Introduction.
- Face Recognition by Elastic Bunch Graph.
- Face Detection and Recognition using Skin Segmentation and Elastic Bunch. Subhradeep (2011) Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching.
- Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose.
Elastic graph matching. Download PDF Opens in a new window. Face recognition by elastic bunch graph matching. An alert was just sent to the Computer Society Digital Library (CSDL) department and we will restore this missing publication as soon as possible.
Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching. Sarkar, Sayantan and Kayal, Subhradeep (2.
Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching. BTech thesis. Recently, face detection and recognition is attracting a lot of interest in areas such as network security, content indexing and retrieval, and video compression, because .
To perform such real- time detection and recognition, novel algorithms are needed, which better current efficiencies and speeds. This project is aimed at developing an efficient algorithm for face detection and recognition. For the detection portion, we present an algorithm based on skin segmentation, morphological operators and template matching. The skin segmentation isolates the face- like regions in a complex image and the following operations of morphology and template matching help reject false matches and extract faces from regions containing multiple faces. For identifying faces, this system uses single images out of a database having one image per person. The task is complex because of variation in terms of position, size, expression, and pose.
The system decreases this variance by extracting face descriptions in the form of image graphs. In this, the node points (chosen as eyes, nose, lips and chin) are described by sets of wavelet components (called . Image graph extraction is based on an approach called the . Recognition is based on a directly comparing these graphs. The advantage of this method is in its tolerance to lighting conditions and requirement of less number of images per person in the database for comparison.