Fusion of shape and texture features for lip biometry in mobile devices
The article presented an HMM-based mm lip recognition for limited users of a handheld device. Tests are made on two small databases. The balanced accuracy in case of three, five, and ten classes are observed. While accuracy in case of three classes (two users) is approximately 99%, it falls to approximately 90% when ten classes (nine users) are considered. From the confusion matrices, it is evident that this fall of accuracy of 10% is due to the increase in number of classes. As this research focuses on use of handheld device by limited number of users, this limitation of scalability is not an issue. This approach can satisfactorily produce performance in considered situation. For practically using this methodology, any template replacement algorithm can be embedded into the biometric system to overcome slight challenges faced due to seasonal change of lip.
Fusion of shape and texture features for lip biometry in mobile devices, Page 1 of 2
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