Person recognition plays an important role in our society and world. This can be observed in varieties of application scenarios such as access control, data management, national ID and forensics. The typical approaches for linking an individual to his/her identity are based on the personal possessions or knowledge, which have the disadvantages of constantly being forgotten, lost or stolen. In the last decades, person identification based on “who you are” has been intensively developed. This is commonly referred to as Biometrics. In this field, the link between an individual and his/her identity is automatically and uniquely established by a human’s intrinsic physiological or behavioral trait, such as face, iris, fingerprint, palmprint, finger vein pattern, voice, signature, gait and so on. Our research is about online person recognition based on the low-resolution palmprint images. The question is how to construct the discriminative palmprint representation for high recognition performance. It is a challenge due to the intra-class variations and inter-class similarities. Furthermore, the widespread use of biometric systems creates security and privacy risks, which have been concerned with increasing attention recently. To mitigate those risks, template-protection technology has been developed as a solution to safeguarding the stored biometric templates. For its successful implementation, the biometrical representation is generally required to be quantized into bits, which are expected to be as discriminative and reliable as possible. This is challenging since the biometric data is highly noisy. Therefore, how to construct reliable binary palmprint representations is investigated in our research work. The major palmprint characteristics are lines and wrinkles. In this thesis, these low-resolution palmprint images are treated as texture images. Accordingly, texture analysis technologies are mainly investigated for palmprint representation. The involved strategies mainly include the multi-scale and multi-orientational transform, region or pixel based statistical features extraction, feature reduction, feature quantization and bits-selection.
|Award date||5 Jul 2013|
|Place of Publication||Enschede|
|Publication status||Published - 5 Jul 2013|