Kevin W. Bowyer - Human Perception of Images

Similarity of Iris Texture Between Siblings
Will Vranderic and Kevin W. Bowyer,
Biometrics Theory, Applications and Systems (BTAS) Sept 30 - Oct 2, 2013.
pdf of this paper.
Prior research has shown that twins' irises are similar enough in appearance for the untrained human observer to correctly determine whether an iris image pair comes from twins or from unrelated persons. We conducted a similar image pair classification study that asked participants to classify an image pair as ''siblings'' or ''unrelated''. We found that untrained human observers can classify pairs of siblings with over 57% accuracy using the appearance of the iris alone, without any proximal image content. This result is statistically greater than the accuracy of random guessing, which indicates that there is some degree of inherited texture similarity between siblings. This raises the question of how accurately one could classify siblings based on automated texture analysis.

Identity Verification Using Iris Images: Performance of Human Examiners,
Kevin McGinn, Samuel Tarin and Kevin W. Bowyer,
Biometrics Theory, Applications and Systems (BTAS) Sept 30 - Oct 2, 2013.
pdf of this paper.
We are not aware of any previous systematic investigation of how well human examiners perform at identity verification using the same type of images as acquired for automated iris recognition. This paper presents results of an experiment in which examiners consider a pair of iris images to decide if they are either (a) two images of the same eye of the same person, or (b) images of two different eyes, with the two different individuals having the same gender, ethnicity and approximate age. Results suggest that novice examiners can readily achieve accuracy exceeding 90% and can exceed 96% when they judge their decision as ''certain''. Results also suggest that examiners may be able to improve their accuracy with experience.

Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light,
Karen P. Hollingsworth, Shelby S. Darnell, Philip E. Miller, Damon L. Woodard, Kevin W. Bowyer and Patrick J. Flynn,
IEEE Transactions on Information Forensics and Security 7 (2), 588-601, April 2012.
pdf of this paper.
... Previous periocular research has used either visible light or near-infrared light images, but no prior research has directly compared the two illuminations using images with similar resolution. We conducted an experiment in which volunteers were asked to compare pairs of periocular images. ... We calculated performance of three computer algorithms on the periocular images. Performance for humans and computers was similar.

Useful Features for Human Verification in Near-Infrared Periocular Images,
Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn,
Image and Vision Computing Journal 29 (11), October 2011, 707-715.
pdf of this paper.
... We conducted two experiments to determine how humans analyze periocular images. In these experiments, we presented pairs of images and asked volunteers to determine whether the two images showed eyes from the same subject or from different subjects. ...
Reprinted in the Journal of Intelligence Community Research and Development.

A Study of Face Recognition of Identical Twins By Humans,
Soma Biswas, Kevin W. Bowyer and Patrick J. Flynn,
International Workshop on Information Forensics and Security (WIFS 2011), December 2011 Foz do Iguacu, Brazil.
pdf of this paper.
In this work, we investigate human capability to distinguish between identical twins. If humans are able to distinguish between facial images of identical twins, it would suggest that humans are capable of identifying discriminating facial traits that can potentially be useful to develop algorithms for this very challenging problem. Experiments with different viewing times and imaging conditions are conducted to determine if humans viewing a pair of facial images can perceive if the image pairs belong to the same person or to a pair of identical twins. The experiments are conducted on 186 twin subjects, making it the largest such study in the literature to date.

What Surprises Do Identical Twins Have for Identity Science?,
Kevin W. Bowyer,
IEEE Computer 44 (7), July 2011, 100-102.
DOI link to this paper.
Experiments with biometric datasets from identical twins are helping to shape future research in face and iris recognition.

Genetically Identical Irises Have Texture Similarity That Is Not Detected By Iris Biometrics,
Karen Hollingsworth, Kevin W. Bowyer, Stephen Lagree, Samuel P. Fenker and Patrick J. Flynn,
Computer Vision and Image Understanding 115, 1493-1502, 2011.
pdf of this paper.
As the standard iris biometric algorithm "sees" them, the left and right irises of the same person are as different as irises of unrelated people. Similarly, in terms of iris biometric matching, the eyes of identical twins are as different as irises of unrelated people. The left and right eyes of an individual or the eyes of identical twins are examples of genetically identical irises. In experiments with human observers viewing pairs of iris images acquired using an iris biometric system, we have found that there is recognizable similarity in the left and right irises of an individual and the irises of identical twins. This result suggests that iris texture analysis different from that performance in the standard iris biometric algorithm may be able to answer questions that iris biometrics cannot answer.

Distinguishing Identical Twins By Face Recognition,
P. Jonathon Phillips, Patrick J. Flynn, Kevin W. Bowyer, Richard W. Vorder Bruegge, Patrick J. Grother, George W. Quinn, Matthew Pruitt,
IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), March 2011, 185-192.
pdf of this paper.
This paper measures ability of face recognition algorithms to distinguish between identical twin siblings. The experimental dataset consists of images taken of 126 pairs of identical twins (252 people) collected on the same day and 24 pairs of identical twins (48 people) with images collected one year apart. Recognition experiments are conducted using three of the top submissions to the Multiple Biometric Evaluation (MBE) 2010 Still Face Track. ...

Human Perceptual Categorization of Iris Texture Patterns,
Louise Stark, Kevin W. Bowyer and Stephen Siena,
Fourth IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2010. DOI: 10.1109/BTAS.2010.5634480
pdf of this paper.
We report on an experiment in which observers were asked to browse a set of 100 iris images and group them into categories based on similarity of overall texture appearance. Results indicate that there is a natural categorization of iris images into a small number of high-level categories, and then also into sub-categories. ...

Human Versus Biometric Perception of Iris Texture,
Presentation only of work done with Karen Hollingsworth, Steve Lagree, Sam Fenker and Patrick J. Flynn,
Biometrics Consortium Conference (BCC), September 2010, Tampa, FL.
pdf of slides.

Are Edges Sufficient for Object Recognition?,
Thomas A. Sanocki, Kevin W. Bowyer, Michael Heath, and Sudeep Sarkar.
Journal of Experimental Psychology: Human Perception and Performance 24 (1), 340-349, January 1998.
pdf of this paper.
The authors argue that the concept of `edges' as used in current research on object recognition obscures the significant difficulties involved in interpreting stimulus information. ... With 1-s exposures, the accuracy of identifying objects in the edge images was found to be less than half that with color photographs. Therefore, edges are far from being sufficient for object recognition.

Comparison of Edge Detectors: A Methodology and Initial Study,
Michael Heath, Sudeep Sarkar, Thomas A. Sanocki and Kevin W. Bowyer.
Computer Vision and Image Understanding 69, (1), 38-54, January 1998.
DOI link.
Because of the difficulty of obtaining ground truth for real images, the traditional technique for comparing low-level vision algorithms is to present image results, side by side, and to let the reader subjectively judge the quality. This is not a scientifically satisfactory strategy. However, human rating experiments can be done in a more rigorous manner to provide useful quantitative conclusions. We present a paradigm based on experimental psychology and statistics, in which humans rate the output of low level vision algorithms. We demonstrate the proposed experimental strategy by comparing four well-known edge detectors: ...

A Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms,
Michael Heath, Sudeep Sarkar, Thomas A. Sanocki, and Kevin W. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (12), 1338-1359, December 1997.
pdf of this paper.
A new method for evaluating edge detection is presented ... The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. ... The novel aspect of this work is the use of a visual task and real images of complex scenes in evaluating edge detectors.