Face Recognition in Color Using Complex and Hypercomplex Representation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-72847-4_29Color has plenty of discriminative information that can be used to improve the performance of face recognition algorithms, although it is difficult to use it because of its high variability. In this paper we investigate the use of the quaternion representation of a color image for face recognition. We also propose a new representation for color images based on complex numbers. These two color representation methods are compared with the traditional grayscale and RGB representations using an eigenfaces based algorithm for identity verification. The experimental results show that the proposed method gives a very significant improvement when compared to using only the illuminance information.Work supported by the Spanish Project DPI2004-08279-C02-02 and the Generalitat Valenciana - Consellería d’Empresa, Universitat i Ciència under an FPI scholarship.Villegas, M.; Paredes Palacios, R. (2007). Face Recognition in Color Using Complex and Hypercomplex Representation. En Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I. Springer Verlag (Germany). 217-224. https://doi.org/10.1007/978-3-540-72847-4_29S217224Yip, A., Sinha, P.: Contribution of color to face recognition. Perception 31(5), 995–1003 (2002)Torres, L., Reutter, J.Y., Lorente, L.: The importance of the color information in face recognition. In: ICIP, vol. 3, pp. 627–631 (1999)Jones III, C., Abbott, A.L.: Color face recognition by hypercomplex gabor analysis. In: FG2006, University of Southampton, UK, pp. 126–131 (2006)Hamilton, W.R.: On a new species of imaginary quantities connected with a theory of quaternions. In: Proc. Royal Irish Academy, vol. 2, pp. 424–434 (1844)Zhang, F.: Quaternions and matrices of quaternions. Linear Algebra And Its Applications 251(1-3), 21–57 (1997)Pei, S., Cheng, C.: A novel block truncation coding of color images by using quaternion-moment preserving principle. In: ISCAS, Atlanta, USA, vol. 2, pp. 684–687 (1996)Sangwine, S., Ell, T.: Hypercomplex fourier transforms of color images. In: ICIP, Thessaloniki, Greece, vol. 1, pp. 137–140 (2001)Bihan, N.L., Sangwine, S.J.: Quaternion principal component analysis of color images. In: ICIP, Barcelona, Spain, vol. 1, pp. 809–812 (2003)Chang, J.-H., Pei, S.-C., Ding, J.J.: 2d quaternion fourier spectral analysis and its applications. In: ISCAS, Vancouver, Canada, vol. 3, pp. 241–244 (2004)Li, S.Z., Jain, A.K.: 6. In: Handbook of Face Recognition. Springer (2005)Gross, R., Brajovic, V.: An image preprocessing algorithm for illumination invariant face recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, p. 1055. Springer, Heidelberg (2003)Lee, K., Ho, J., Kriegman, D.: Nine points of light: Acquiring subspaces for face recognition under variable lighting. In: CVPR, vol. 1, pp. 519–526 (2001)Zhang, L., Samaras, D.: Face recognition under variable lighting using harmonic image exemplars. In: CVPR, vol. 1, pp. 19–25 (2003)Villegas, M., Paredes, R.: Comparison of illumination normalization methods for face recognition. In: COST 275, University of Hertfordshire, UK, pp. 27–30 (2005)Turk, M., Pentland, A.: Face recognition using eigenfaces. In: CVPR, Hawaii, pp. 586–591 (1991)Bihan, N.L., Mars, J.: Subspace method for vector-sensor wave separation based on quaternion algebra. In: EUSIPCO, Toulouse, France (2002)XM2VTS (CDS00{1,6}), http://www.ee.surrey.ac.uk/Reseach/VSSP/xm2vtsdbLuettin, J., Maître, G.: Evaluation protocol for the extended M2VTS database (XM2VTSDB). IDIAP-COM 05, IDIAP (1998

RGBD Datasets: Past, Present and Future

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms