### Abstract

In this paper we present a unified graph model, called Attributed Relational Graph (ARG), for multi-modal face modeling and recognition. Based on the ARG model, the 2-D and 3-D data are included in a single model. The developed ARG model consists of nodes, edges, and mutual relations. The nodes of the graph correspond to the landmark points that are extracted by an improved Active Shape Model (ASM) technique. Then, at each node of the graph, the responses of a set of log-Gabor filters to the facial image texture and shape information (depth values) are calculated; the filter responses are used to model the local structure of the face at each node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of mutual relations between the sides of the triangles are defined. The mutual relations boost the final performance of the system. The results of face matching using the 2-D and 3-D attributes and the mutual relations are fused at the score level. A rank-one identification rate of 99% is achieved by experimenting on the University of Miami face database.

Original language | English |
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Title of host publication | Proceedings - International Conference on Image Processing, ICIP |

Pages | 2760-2763 |

Number of pages | 4 |

DOIs | |

State | Published - Dec 1 2008 |

Event | 2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States Duration: Oct 12 2008 → Oct 15 2008 |

### Other

Other | 2008 IEEE International Conference on Image Processing, ICIP 2008 |
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Country | United States |

City | San Diego, CA |

Period | 10/12/08 → 10/15/08 |

### Fingerprint

### Keywords

- Attributed Relational Graph
- Data fusion
- Multi-modal face recognition

### ASJC Scopus subject areas

- Software
- Computer Vision and Pattern Recognition
- Signal Processing

### Cite this

*Proceedings - International Conference on Image Processing, ICIP*(pp. 2760-2763). [4712366] https://doi.org/10.1109/ICIP.2008.4712366

**Multi-modal (2-D and 3-D) face modeling and recognition using attributed relational graph.** / Mahoor, Mohammad H.; Ansari, A. Nasser; Abdel-Mottaleb, Mohamed.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings - International Conference on Image Processing, ICIP.*, 4712366, pp. 2760-2763, 2008 IEEE International Conference on Image Processing, ICIP 2008, San Diego, CA, United States, 10/12/08. https://doi.org/10.1109/ICIP.2008.4712366

}

TY - GEN

T1 - Multi-modal (2-D and 3-D) face modeling and recognition using attributed relational graph

AU - Mahoor, Mohammad H.

AU - Ansari, A. Nasser

AU - Abdel-Mottaleb, Mohamed

PY - 2008/12/1

Y1 - 2008/12/1

N2 - In this paper we present a unified graph model, called Attributed Relational Graph (ARG), for multi-modal face modeling and recognition. Based on the ARG model, the 2-D and 3-D data are included in a single model. The developed ARG model consists of nodes, edges, and mutual relations. The nodes of the graph correspond to the landmark points that are extracted by an improved Active Shape Model (ASM) technique. Then, at each node of the graph, the responses of a set of log-Gabor filters to the facial image texture and shape information (depth values) are calculated; the filter responses are used to model the local structure of the face at each node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of mutual relations between the sides of the triangles are defined. The mutual relations boost the final performance of the system. The results of face matching using the 2-D and 3-D attributes and the mutual relations are fused at the score level. A rank-one identification rate of 99% is achieved by experimenting on the University of Miami face database.

AB - In this paper we present a unified graph model, called Attributed Relational Graph (ARG), for multi-modal face modeling and recognition. Based on the ARG model, the 2-D and 3-D data are included in a single model. The developed ARG model consists of nodes, edges, and mutual relations. The nodes of the graph correspond to the landmark points that are extracted by an improved Active Shape Model (ASM) technique. Then, at each node of the graph, the responses of a set of log-Gabor filters to the facial image texture and shape information (depth values) are calculated; the filter responses are used to model the local structure of the face at each node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of mutual relations between the sides of the triangles are defined. The mutual relations boost the final performance of the system. The results of face matching using the 2-D and 3-D attributes and the mutual relations are fused at the score level. A rank-one identification rate of 99% is achieved by experimenting on the University of Miami face database.

KW - Attributed Relational Graph

KW - Data fusion

KW - Multi-modal face recognition

UR - http://www.scopus.com/inward/record.url?scp=69949176374&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=69949176374&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2008.4712366

DO - 10.1109/ICIP.2008.4712366

M3 - Conference contribution

SN - 1424417643

SN - 9781424417643

SP - 2760

EP - 2763

BT - Proceedings - International Conference on Image Processing, ICIP

ER -