Tong-Yuen Chai

Local Chan-Vese segmentation for non-ideal visible wavelength iris images

Iris segmentation becomes a challenging task for non-ideal iris shape captured under visible wavelength environment. In this paper, we proposed a localized active contour model for iris segmentation. A Local Chan-Vese (LCV) region-based active contour model is studied and applied to segment the non-ideal visible wavelength iris images. The proposed localized region-based formulation is more robust and suitable in segmenting the papillary/limbic boundary of non-cooperative users under visible wavelength compared to a standard Chan-Vese (CV) segmentation model which has intrinsic limitation when dealing with inhomogeneity properties. We applied B-spline explicit framework to further improve the computational efficiency of the algorithm overcoming the limitations of the original method. Experimental results on NICE.I have indicated good segmentation accuracy using the proposed algorithm.

Alignment-free indexing-first-one hashing with bloom filter integration

This paper explores the recently published works on iris template protection namely Indexing-First-One hashing. Despite the Indexing-First-One hashing offers high recognition performance with resistant against several major privacy and security attacks, it does not resolve the rotation inconsistent issues existed in conventional iris template due to head tilt/ rotation during users eyes images acquisition. Hence, a pre-alignment step is required for the conventional IFO hashed code matching. Consequently, this increased the computational cost heavily. Hereby, we address the rotation inconsistent issue by proposing an alignment-free IFO hashing through a pre-transformation based on Bloom filter generation. The proposed pre-alignment IFO hashing shows promising recognition performance, and the pre-alignment procedure is eliminated to lower computational cost.

Study of the similarity function in Indexing-First-One hashing

The recent proposed Indexing-First-One (IFO) hashing is a latest technique that is particularly adopted for eye iris template protection, i.e. IrisCode. However, IFO employs the measure of Jaccard Similarity (JS) initiated from Min-hashing has yet been adequately discussed. In this paper, we explore the nature of JS in binary domain and further propose a mathematical formulation to generalize the usage of JS, which is subsequently verified by using CASIA v3-Interval iris database. Our study reveals that JS applied in IFO hashing is a generalized version in measure two input objects with respect to Min-Hashing where the coefficient of JS is equal to one. With this understanding, IFO hashing can propagate the useful properties of Min-hashing, i.e. similarity preservation, thus favorable for similarity searching or recognition in binary space.

Range reconstruction model for 3D gated imaging

Range gated imaging is a remote sensing acquisition which involves the emission of a laser pulse and an intensified camera to gate the reflected laser pulse. Range accuracy has always been an issue especially when a highly accurate reconstructed model is expected as the final outcome. The reflected pulse profile and pulse instability are among the issues that affect the range accuracy when a general solution such as constant offset is not applicable. In this paper, a study to estimate a more accurate model for the reflected pulse profile has been investigated through experiments. T Location-Scale model has been proposed to replace the Gaussian model as the general assumption for range-gated image formation model. The improvement on range accuracy which is around 0.3% has been verified through simulation based on the acquired samples. The series of range-gated images can be reconstructed into a three-dimensional depth map through range calculation. This can be used in the subsequent range reconstruction works.