Search

GJB 1032-1990 has been put into use for more than 20 years which plays an important part in improving the reliability of electronic products. However, for the electronic module, it becomes unpractical in engineering because of costing too much test time and manpower. To improve ESS test efficiency of Electronic module, an ESS test condition tailoring method based on GJB1032 is studied in this paper. Recurring to the RADC model of thermal cycle test, this method is proved in theory. Taking a microwave converter as example, the method is used to tailor its ESS test condition in practice. According to the ESS test result of the microwave converter, the ESS test tailored by the method in this paper can save more half test time and costs than GJB1032 under the same screening strength.

We propose a methodology for cross matching color face images and Short Wave Infrared (SWIR) face images reliably and accurately. We first adopt a recently designed image encoding and matching technique which is capable to encode face images in both visible and SWIR spectral bands. Encoding is performed in two steps. Images are initially filtered with a bank of Gabor filters. Then three local operators: Simplified Weber Local Descriptor and Local Binary Pattern applied to magnitude of filtered images and Generalized Local Binary Pattern applied to the phase are involved to create histogram-like feature templates. The distance between two encoded face images is measured by symmetric I-divergence. The encoding and matching methods are demonstrated on long range SWIR data matched against close range visible images. A considerable performance improvement is observed compared to the results by FaceIt G8. To further enhance performance we propose an adaptive score normalization approach. We demonstrate that significant performance improvement is achieved with a small training set. Matching scores obtained by the proposed normalized method and by FaceIt G8 are fused to result in further performance improvement.

Three methods to improve the performance of biometric matchers based on vectors of quality measures associated with biometric samples are described. The first two methods select samples and matching scores based on predicted values of Quality of Sample (QS) index (defined here as d-prime) and Confidence in matching Scores (CS), respectively. The third method treats quality measures as weak but useful features for discrimination between genuine and imposter matching scores. The unifying theme for the three methods consists of a nonlinear mapping between quality measures and the predicted values of QS, CS, and combined quality measures and matching scores, respectively. The proposed methodology is generic and is suitable for any biometric modality. The experimental results reported show significant performance improvements for all the three methods when applied to iris biometrics.

An adaptive method to predict NIR channel image from color iris images is introduced. Both visual inspection of the predicted image and the verification performance indicate that the adaptive mapping linking NIR image and color image is a potential solution to the problem of matching NIR images vs. color images in practice. When matched against NIR enrolled image the predicted NIR image achieves significantly high performance compared to the case when the same NIR image is matched against R channel alone.

In the field of iris-based recognition, evaluation of quality of images has a number of important applications. These include image acquisition, enhancement, and data fusion. Iris image quality metrics designed for these applications are used as figures of merit to quantify degradations or improvements in iris images due to various image processing operations. This paper elaborates on the factors and introduces new global and local factors that can be used to evaluate iris video and image quality. The main contributions of the paper are as follows. (1) A fast global quality evaluation procedure for selecting the best frames from a video or an image sequence is introduced. (2) A number of new local quality measures for the iris biometrics are introduced. The performance of the individual quality measures is carefully analyzed. Since performance of iris recognition systems is evaluated in terms of the distributions of matching scores and recognition probability of error, from a good iris image quality metric it is also expected that its performance is linked to the recognition performance of the biometric recognition system.

A person only has two irises - if his pattern is stolen he quickly runs out of alternatives. Thus methods that protect the true iris pattern need to be adopted in practical biometric applications. In particular, it is desirable to have a system that can generate a new unique pattern if the one being used is lost, or generate different unique patterns for different applications to prevent cross-matching. For backwards compatibility, these patterns should look like plausible irises so they can be handled with the same processing tools. However, they should also non-invertibly hide the true biometric so it is never exposed, or even stored. In this paper four such ldquocancelablerdquo biometric methods are proposed that work with conventional iris recognition systems, either at the unwrapped image level or at the binary iris code level.

Iris segmentation is an important first step for high accuracy iris recognition. A robust iris segmentation procedure should be able to handle noise, occlusion and non-uniform lighting. It also impacts system accuracy - high FAR or FRR values may come directly from bad or wrong segmentations. In this paper a simple new approach for iris segmentation is proposed that tries to integrate quality evaluation ideas directly into the segmentation algorithm. By cutting out all the bad areas, the fraction of the iris that remains can be used as a comprehensive quality measure. This eliminates images with high occlusion (e.g. by the eyelids) as well as images with other quality problems (e.g. low contrast), all using the same mechanism. The proposed method has been tested on a medium-sized (450 image) public database (MMU1) and the score distribution investigated. We also show that, as expected, overall matching accuracy can be improved by rejecting images which have a low quality assessment, thus validating the utility of this measure.

Iris-based human recognition is very attractive because of the high accuracy achievable. However, existing encoding methods are unable to handle iris images acquired when the ambient lighting is non-uniform. In this paper we propose a novel encoding technique which can handle images acquired under such conditions. The method is based on 2D median filters, which are commonly used as de-noising tools in image processing domain, and uses their nonlinear characteristics to generate binary codes from gray scale iris images. The application of this method for robust iris recognition is discussed and compared with two more traditional iris encoding techniques: the 2D Gabor filter based method and the 1D log-Gabor method. Our algorithm is usable for the whole range of conditions encountered in the MMU1 iris database, performs well in a synthetic uneven illumination study, and gets comparable results on other tests.

Recent developments in the field of nonideal iris recognition have shown that the presence of the degradations such as insufficient contrast, unbalanced illumination, out-of-focus, motion blur, specular reflections, and partial area affect performance of iris recognition systems. Most iris recognition systems are designed to implement a number of processing steps with iris segmentation being one of the first steps. If segmentation is not performed at a certain precision, the error of segmentation will further propagate and will be amplified during the proceeding processing, encoding, and matching steps. This emphasizes a critical need in designing robust iris segmentation algorithms and together with it a need of automatic algorithms evaluating the precision (accuracy) of iris segmentation. Automatic algorithm evaluating the precision of segmentation plays important role for two reasons: (1) it can be placed into a feedback loop to enforce another run of segmentation algorithm that may include more sophisticated steps for high precision segmentation and (2) the outcome of this evaluation can be treated as a quality factor and thus can be used to design a quality driven adaptive iris recognition system. This work analyzes effects of degradations on iris segmentation and proposes and tests an automatic algorithm evaluating the precision of iris segmentation.

Daugman's iris recognition algorithm introduced in early 90s and later undergoing continuous refinements remains potentially the most efficient and scalable in iris field. The encoding part of the algorithm relies on application of Gabor wavelets that in terms of their imaging capabilities mimic capabilities of human eye receptor field. In this work, we design and test an algorithm that can be used both individually and as a natural extension scheme to Gabor wavelet-based algorithm. It is based on the local ordinal information extracted from original unfiltered images. This scheme holds a number of promises: (1) it is robust with respect to a number of nonidealities in iris images and (2) because of the binary nature of the local ordinal information this scheme can be flawlessly integrated into the traditional filter-based recognition systems. The proposed scheme was extensively tested individually and when combined with Gabor wavelet-based approach.

In the absence of real data for extensive testing of newly designed large-scale biometrics recognition systems a number of solutions are possible including use of resampling methods, generation of synthetic data having properties similar to real data of interest, or use of analytical tools to predict the performance. Each of the methods has its own limitations. In this work, we focus on iris biometric. We briefly describe a model based approach to synthesize iris images and focus on performance comparison for synthesized and real iris images. Iris image processing assumes a traditional Gabor filter based encoding approach. Comparison of synthetic and real data is performed at three levels of processing: (1) image level, (2) texture level, and (3) decision level. The results indicate that in most cases the performance of synthesized iris images is comparable to the performance of the real iris images.

This paper presents an open controller which is designed for bending the hull steel plates. It is an industrial robot that is based on the SERCOS bus and carries out a so-called "bending by line heating" procedure in China. To treat with the diversity of the original steel plates' shapes, we designed an open controller for it. The controller is open because it provides a set of instructions with which the end user can compile different programs according to the different working procedures. A positioning method used in the saddle type plate forming is also introduced.