Kevin W. Bowyer - Ear Biometrics

  • Biometric Identification of Identical Twins: A Survey
    Kevin W. Bowyer and Patrick J. Flynn,
    IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2016.
    pdf of this paper.
    The ability of biometric techniques to distinguish between identical twins is of interest for multiple reasons. The research literature touching on this topic is spread across a variety of areas. This survey pulls together the literature to date in this area, identifies available datasets for research, points out topics of uncertainty and suggests possible future research.

  • Profile Face Detection: A Subset Multi-biometric Approach,
    James Gentile, Kevin W. Bowyer and Patrick J. Flynn,
    Biometrics: Theory, Applications and Systems (BTAS 08), September 2008, Washington, DC.
    DOI link.

  • A Fast Algorithm for ICP-based 3D Shape Biometrics,
    Ping Yan and Kevin W. Bowyer,
    Computer Vision and Image Understanding, 107 (3), 195-202, September 2007.
    pdf of this paper.
    ... we present a novel approach, called "Pre-computed Voxel Nearest Neighbor," to reduce the computational time for shape matching in a biometrics context. The approach shifts the heavy computation burden to the enrollment stage, which is done offline. Experiments in 3D ear biometrics with 369 subjects and 3D face biometrics with 219 subjects demonstrate the effectiveness of our approach.

  • Biometric Recognition Using 3D Ear Shape,
    Ping Yan and Kevin W. Bowyer.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (8), 1297-1308, August 2007.
    pdf of this paper.
    ... We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8% for an identification scenario, and equal error rate of 1.2% for a verification scenario on a database of 415 subjects and 1,386 total probes.

  • Empirical Evaluation of Advanced Ear Biometrics,
    Ping Yan and Kevin W. Bowyer,
    Workshop on Empirical Evaluation Methods in Computer Vision, San Diego, CA, June 2005.
    pdf of this paper.
    We present results of the largest experimental investigation of ear biometrics to date. Approaches considered include a PCA ("eigen-ear") approach with 2D intensity images, achieving 63.8% rank-one recognition; a PCA approach with range images, achieving 55.3% Hausdorff matching of edge images from range images, achieving 67.5% and ICP matching of the 3D data, achieving 98.7%. ICP based matching not only achieves the best performance, but also shows good scalability with size of dataset. The data set used represents over 300 persons, each with images acquired on at least two different dates. In addition, the ICP-based approach is further applied on an expanded data set of 404 subjects, and achieves 97.5% rank one recognition rate. In order to test the robustness and variability of ear biometrics, ear symmetry is also investigated. In our experiments around 90% of peoples' right and left ears are symmetric.

  • Comparison and Combination of Ear and Face Images for Appearance-based Biometrics,
    Kyong Chang, Kevin W. Bowyer, Sudeep Sarkar, and Barnabas Victor,
    IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (9), September 2003, 1160-1165.
    pdf of this paper.
    In the experiments reported here, recognition performance is essentially identical using ear images or face images and combining the two for multimodal recognition results in a statistically significant performance improvement. ... To our knowledge, ours is the only work to present any experimental results of computer algorithms for biometric recognition based on the ear.