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Influence of the Intra-Modal Facial Information for an Identification Approach

Influence of the Intra-Modal Facial Information for an Identification Approach

Carlos M. Travieso, Marcos del Pozo-Baños, Jaime R. Ticay-Rivas, Jesús B. Alonso
Copyright: © 2014 |Pages: 25
ISBN13: 9781466658080|ISBN10: 1466658088|EISBN13: 9781466658097
DOI: 10.4018/978-1-4666-5808-0.ch013
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MLA

Travieso, Carlos M., et al. "Influence of the Intra-Modal Facial Information for an Identification Approach." Multidisciplinary Perspectives in Cryptology and Information Security, edited by Sattar B. Sadkhan Al Maliky and Nidaa A. Abbas, IGI Global, 2014, pp. 318-342. https://doi.org/10.4018/978-1-4666-5808-0.ch013

APA

Travieso, C. M., del Pozo-Baños, M., Ticay-Rivas, J. R., & Alonso, J. B. (2014). Influence of the Intra-Modal Facial Information for an Identification Approach. In S. Sadkhan Al Maliky & N. Abbas (Eds.), Multidisciplinary Perspectives in Cryptology and Information Security (pp. 318-342). IGI Global. https://doi.org/10.4018/978-1-4666-5808-0.ch013

Chicago

Travieso, Carlos M., et al. "Influence of the Intra-Modal Facial Information for an Identification Approach." In Multidisciplinary Perspectives in Cryptology and Information Security, edited by Sattar B. Sadkhan Al Maliky and Nidaa A. Abbas, 318-342. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5808-0.ch013

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Abstract

This chapter presents a comprehensive study on the influence of the intra-modal facial information for an identification approach. It was developed and implemented a biometric identification system by merging different intra-multimodal facial features: mouth, eyes, and nose. The Principal Component Analysis, Independent Component Analysis, and Discrete Cosine Transform were used as feature extractors. Support Vector Machines were implemented as classifier systems. The recognition rates obtained by multimodal fusion of three facial features has reached values above 97% in each of the databases used, confirming that the system is adaptive to images from different sources, sizes, lighting conditions, etc. Even though a good response has been shown when the three facial traits were merged, an acceptable performance has been shown when merging only two facial features. Therefore, the system is robust against problems in one isolate sensor or occlusion in any biometric trait. In this case, the success rate achieved was over 92%.

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