Abstract
This paper presents a multimodal biometric system based on face and hand images captured by a cell phone. The multimodal fusion is done at the feature extraction level. The nine facial models are built according to the number of features / points extracted from the face. Active shape models method is applied in order to find the concatenated string of facial points in the eyes, nose, and mouth areas. The face feature vector is constructed by applying Gabor filter to the image and extracting the key points found by an active shape model. The hand feature vector contains nine geometric measurements, including heights and widths of four fingers, and the width of the palm. Support vector machine is used as a classifier for a multimodal approach. One SVM machine is built for each person in the database to distinguish that person from the others. The database contains 113 individuals. As the experiments show, the best accuracy of up to 99.82% has been achieved for the model combining 8 eye, 12 mouth and 9 hand features.
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Rokita, J., Krzyżak, A., Suen, C.Y. (2008). Cell Phones Personal Authentication Systems Using Multimodal Biometrics. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_101
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DOI: https://doi.org/10.1007/978-3-540-69812-8_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
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