Browsing by Author "Zhou, Zhi"

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    A New Approach for Cancelable Iris Recognition
    (Office of the Vice Chancellor for Research, 2010-04-09) Yang, Kai; Yan, Sui; Zhou, Zhi; Du, Yingzi; Zou, Xukai; Orr, Scott
    The iris is a stable and reliable biometric for positive human identification. However, the traditional iris recognition scheme raises several privacy concerns. One’s iris pattern is permanently bound with him and cannot be changed. Hence, once it is stolen, this biometric is lost forever as well as all the applications where this biometric is used. Thus, new methods are desirable to secure the original pattern and ensure its revocability and alternatives when compromised. In this paper, we propose a novel scheme which incorporates iris features, noninvertible transformation and data encryption to achieve “cancelability” and at the same time increases iris recognition accuracy.
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    Scale Invariant Gabor Descriptor-Based Noncooperative Iris Recognition
    (SpringerOpen, 2010-04-28) Du, Yingzi; Belcher, Craig; Zhou, Zhi; Electrical and Computer Engineering, School of Engineering and Technology
    A new noncooperative iris recognition method is proposed. In this method, the iris features are extracted using a Gabor descriptor. The feature extraction and comparison are scale, deformation, rotation, and contrast-invariant. It works with off-angle and low-resolution iris images. The Gabor wavelet is incorporated with scale-invariant feature transformation (SIFT) for feature extraction to better extract the iris features. Both the phase and magnitude of the Gabor wavelet outputs were used in a novel way for local feature point description. Two feature region maps were designed to locally and globally register the feature points and each subregion in the map is locally adjusted to the dilation/contraction/deformation. We also developed a video-based non-cooperative iris recognition system by integrating video-based non-cooperative segmentation, segmentation evaluation, and score fusion units. The proposed method shows good performance for frontal and off-angle iris matching. Video-based recognition methods can improve non-cooperative iris recognition accuracy.