Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images.
However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been
shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera
recognition together can achieve better recognition accuracy. However, image quality can significantly affect
the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally
facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition
method that combine the respective strengths of iris recognition and sclera recognition for human
identification and can work with frontal and off-angle eye images. The research results show that the
proposed method is very promising.
Traditional iris recognition algorithms can work well for the frontal iris images.
However, when the gaze of an eye changes with respect to the camera lens, many times the size,
shape, and detail of iris patterns will change as well and cannot be matched to enrolled images using
traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes
much more challenging and noncooperative iris algorithms will require a different approach. In this
paper, we propose a new approach for iris recognition. This new method does not require polar
transformation, affine transformation or highly accurate segmentation to perform iris recognition.
Our research results using the remote non-cooperative iris Image database show that the proposed
method works well on frontal look images and off-angle images as well.
Poor quality can affect iris recognition accuracy. Feature information is an objective measure to evaluate the iris image
quality. By combining Feature Information Measure (FIM), an occlusion measure and a dilation measure, a quality score
is obtained that is well correlated with recognition accuracy. FIM is calculated as the distance between the distribution
of iris features and a uniform distribution. Images of low contrast can appear to lack information from manual inspection,
but actually perform well in iris recognition due to the presence of feature information. However, the FIM score for a
low contrast image could be low. To adjust this affect, this paper developed an information based contrast invariant iris
quality measure. For exhaustive comparison, CASIA 1.0, CASIA 2.0, ICE and WVU databases is used. In addition,
the proposed method is compared to the convolution matrix, spectrum energy and Mexican hat wavelet approaches
which represent a variety of approaches to iris quality measure. The experimental results show that the proposed quality
measure is capable of predicting matching performance.
Iris recognition systems have been tested to be the most accurate biometrics systems.
However, poor quality images greatly affect accuracy of iris recognition systems. Many
factors can affect the quality of an iris image, such as blurriness, resolution, image
contrast, iris occlusion, and iris deformation, but blurriness is one of the most significant
problems for iris image acquisition. In this paper, we propose a new method to measure
the blurriness of an iris image called information distance based selective feature clarity
measure. Different from any other approach, the proposed method automatically selects
portions of the iris with most changing patterns to measure the level of blurriness based
on their frequency characteristics. Log-Gabor wavelet is used to capture the features of
the selected portions. By comparing the information loss from the original features to
blurred versions of the same features, the algorithm decides the clarity of the original iris
image. The preliminary experiment results show that this method is effective.
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