Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition

Iris recognition algorithms, especially with the emergence of large-scale iris-based identification systems, must be tested for speed and accuracy and evaluated with a wide range of templates – large size, long-range, visible and different origins. This paper presents the acquisition of eye-iris images of dark-skinned subjects in Africa, a predominant case of verydark- brown iris images, under near-infrared illumination. The peculiarity of these iris images is highlighted from the histogram and normal probability distribution of their grayscale image entropy (GiE) values, in comparison to Asian and Caucasian iris images. The acquisition of eye-images for the African iris dataset is ongoing and will be made publiclyavailable as soon as it is sufficiently populated

ANALYSIS OF FACIAL MARKS TO DISTINGUISH BETWEEN IDENTICAL TWINS USING NOVEL METHOD

Reliable and accurate verification of people is extremely important in a number of business transactions as well as access to privileged information. The biometrics-based methods assume that the physical characteristics of an individual (as captured by a sensor) used for verification are sufficiently unique to distinguish one person from another. But the increase in twin births has created a requirement for biometric systems to accurately determine the identity of a person who has an identical twin. Identical twins have the closest genetics-based relationship and, therefore, the maximum similarity between fingerprints is expected to be found among identical twins. They can’t be discriminated based on DNA. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. Identical twin face recognition is a difficult task due to the existence of a high degree of correlation in overall facial appearance. In this paper, we study the usability of facial marks as biometric signatures to distinguish between identical twins. We propose a multi scale automatic facial mark detector based on a gradient-based operator known as the fast radial symmetry transform. The transform detects bright or dark regions with high radial symmetry at different scales. Next, the detections are tracked across scales to determine the prominence of facial marks. Extensive experiments are performed both on manually annotated and on automatically detected facial marks to evaluate the usefulness of facial marks as biometric signatures. The results of our analysis signify the usefulness of the distribution of facial marks as a biometric signature

Broken Symmetries, Random Morphogenesis, and Biometric Distance

This paper discusses the role of symmetry-breaking in biometric recognition. Using publicly available databases, we investigate three kinds of broken symmetries in iris patterns: binocular, monocular, and monozygotic. We report a small but statistically significant difference in similarities between the ipsilateral and the contralateral eyes of twins, and also between genetically identical and nonidentical eyes. Another new finding is a doubling in the variance of Hamming distance scores under a simple monocular mirror transformation, which is consistent with an assessment of entropy

Twin identification over viewpoint change: A deep convolutional neural network surpasses humans

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN

IRIS RECOGNITION FAILURE IN BIOMETRICS: A REVIEW

More than twenty years iris has been claimed to be the most stable modality in human lifetime. However, the iris recognition produces ‘failure to match’ problem which made the known is unknown user or the genuine is recognized as imposter in the biometric systems. Apparently, failure to recognize the real user as in the database is due to a few assumptions: aging of the sensor, changes in how a person uses the system such as the threshold settings and template aging effect. This paper focuses on template aging effect since it is on ongoing problem faced in iris recognition. Many studies attempted several techniques to overcome the problem in every phase which consists of three general phases: the pre-processing, feature extraction and feature matching. Therefore, the purpose of this paper is to study and identify the problems in iris recognition that lead to failure-to-match in biometrics

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry and fingerprint recognition. In biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental result showed that iris recognition can distinguish twin as it can distinguish two different, unrelated people as the result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75% and 96.48% respectively

Heredity in identical twins

This item was digitized by the Internet Archive. Thesis (M.A.)--Boston Universityhttps://archive.org/details/heredityinidenti00gaw

LBP-based periocular recognition on challenging face datasets

This work develops a novel face-based matcher composed of a multi-resolution hierarchy of patch-based feature descriptors for periocular recognition - recognition based on the soft tissue surrounding the eye orbit. The novel patch-based framework for periocular recognition is compared against other feature descriptors and a commercial full-face recognition system against a set of four uniquely challenging face corpora. The framework, hierarchical three-patch local binary pattern, is compared against the three-patch local binary pattern and the uniform local binary pattern on the soft tissue area around the eye orbit. Each challenge set was chosen for its particular non-ideal face representations that may be summarized as matching against pose, illumination, expression, aging, and occlusions. The MORPH corpora consists of two mug shot datasets labeled Album 1 and Album 2. The Album 1 corpus is the more challenging of the two due to its incorporation of print photographs (legacy) captured with a variety of cameras from the late 1960s to 1990s. The second challenge dataset is the FRGC still image set. Corpus three, Georgia Tech face database, is a small corpus but one that contains faces under pose, illumination, expression, and eye region occlusions. The final challenge dataset chosen is the Notre Dame Twins database, which is comprised of 100 sets of identical twins and 1 set of triplets. The proposed framework reports top periocular performance against each dataset, as measured by rank-1 accuracy: (1) MORPH Album 1, 33.2%; (2) FRGC, 97.51%; (3) Georgia Tech, 92.4%; and (4) Notre Dame Twins, 98.03%. Furthermore, this work shows that the proposed periocular matcher (using only a small section of the face, about the eyes) compares favorably to a commercial full-face matcher