3D Facial Recognition with Soft Computing

• Published in: Digital Human Modeling pp. 194 - 205
• Main Authors: Lee, Yeung-Hak, Han, Chang-Wook, Kim, Tae-Sun
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2008
• Series: Lecture Notes in Computer Science
• Subjects: 3D Face; Fuzzy Neural Networks
• ISBN: 3540894292; 9783540894292
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-89430-8_11

The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss of the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. The normalized facial images are also considered to enhance the recognition rate. To classify the faces, the cascade architectures of fuzzy neural networks, which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. Experimental results on a 46 persons data set of 3D images demonstrate the effectiveness of the proposed method.