SURF and MU-SURF descriptor comparison with application in soft-biometric tattoo matching applications

  1. Iturbe, Mikel 2
  2. Uribeetxeberria, Roberto 2
  3. Kähm, Olga 1
  1. 1 Fraunhofer Institute for Computer Graphics Research IGD
  2. 2 Universidad de Mondragón/Mondragon Unibertsitatea
    info

    Universidad de Mondragón/Mondragon Unibertsitatea

    Mondragón, España

    ROR https://ror.org/00wvqgd19

Actas:
Reunión Española sobre Criptología y Seguridad de la Información (RECSI). Mondragon. 4-7 Septiembre,

Editorial: Mondragon Unibertsitatea

ISBN: 9788461599332

Año de publicación: 2012

Páginas: 5

Congreso: Reunión Española sobre Criptología y Seguridad de la Información (RECSI). Mondragon. 4-7 Septiembre

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openeBiltegia editor

Resumen

In this work a comparison of the SURF and MUSURF feature descriptor vectors is made. First, the descriptors’ performance is evaluated using a standard data set of general transformed images. This evaluation consists in counting correspondences and correct matches between ten image pairs. Image pairs have different transformations (rotation, scale change, viewpoint change, blur, JPEG compression and illumination change) in order to evaluate the descriptors in different environments. The second test evaluates the descriptors’ suitability for tattoo matching. In this case, one hundred randomly chosen transformed tattoo images are matched against a database of ten thousand images. The transformations include rotation change, RGB noise and cropped images. Non-transformed images are also evaluated. In both tests, the descriptors represent the interest points previously detected and stored into a file by the same detector, to ensure the validity of the test. Results show that the newer and modified version of the SURF descriptor, MU-SURF, performs better than its counterpart and it is suitable for tattoo matching.

Referencias bibliográficas

  • [1] J.-E. Lee, R. Jin, and A. K. Jain, “Image Retrieval in Forensics: Tattoo Image Database Application,” IEEE MultiMedia, vol. 19, pp. 40–49, January 2012.
  • [2] D. Manger, “Large-Scale Tattoo Image Retrieval,” in Proceedings of the Ninth Conference on Computer and Robot Vision, CRV 2012, (Toronto, Ontario, Canada), May 2012.
  • [3] D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision (IJCV), vol. 2, no. 60, pp. 91– 100, 2004.
  • [4] H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” Computer Vision and Image Understanding (CVIU), vol. 110, pp. 346– 359, 2008.
  • [5] L. Juan and O. Gwun, “A comparison of SIFT, PCA-SIFT and SURF,” International Journal of Image Processing (IJIP), vol. 3, no. 4, pp. 143– 152, 2009.
  • [6] M. Agrawal, K. Konolige, and M. R. Blas, “CenSurE: Center surround extremas for realtime feature detection and matching,” in ECCV (4), vol. 5305 of Lecture Notes in Computer Science, pp. 102–115, Springer, October 2008.
  • [7] K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1615–1630, October 2005.
  • [8] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, “A comparison of affine region detectors,” International Journal of Computer Vision, vol. 65, no. 1, pp. 43–72, 2005.