FACE RECOGNITION

different number of classes (subjects). In our experiments,we vary the number of classes between 25 and 150 as depictedin Figure 6.From this figure, we can see that when the number ofclasses varies between 25 and 50, the performance of bothsystems in terms of predictive accuracy is close to maximal.Ho[r]

the system must be fast enough to capture all the relevant information de-rived from video images. If the computation takes too long, the person mightbe gone, or might have a different expression. The need for real-time appli-cability thus demands for high performance and efficiency of applications for[r]

known as principal components analysis) of observed distri-butions in the color space to determine the improved map-ping.Optimization of Color Conversion for Face Recognition 523Other work [4] has suggested that alternative color spacesprovide no real benefit for locating skin in images[r]

[16] J. G. Daugman, “Complete discrete 2-D Gabor transforms byneural networks for image analysis and compression,” IEEETrans. Acoustics, Speech, and Signal Processing, vol. 36, no. 3,pp. 1169–1179, 1988.[17] J. Huang, P. C. Yuen, and J. H. Lai, “Combining local andglobal features for face [r]

face is calculated. Finally, the third is based on the use of laserrange finding systems to capture the 3D facial surface. Thethird technique has the best reliability and resolution whilethe first has relatively poor robustness and accuracy. The at-traction of passive stereoscopy is in its noni[r]

the three multiband methods discussed in the previousparagraph where each algorithm uses only the first threebands. It is interesting that sorting the bands according toperformance improves the recognition rate for N= 1butworsens the performance somewhat for larger values of N.Ineither case, t[r]

u= u cos θ + v sinθ,v= v cos θ −u sin θ.(2)Figures 1(a) and 1(b) show, respectively, the real part andmagnitude of the Gabor wavelets for 4 scales and 6 directions.Figure 2 shows the 1D Gabor wavelets in the frequencydomain. At all levels, the wavelet is a Gaussian bandpass filter.Gabor wavelets po[r]

ventional PCA-based face recognition system and state-of-arttechniques such as Nonnegative Matrix Factorization (NMF)[26, 27], supervised incremental NMF (INMF) [28], LBP [8],and LDA [3] based face recognition systems for the FERETface database. The experimental results a[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2008, Article ID 312849, 11 pagesdoi:10.1155/2008/312849Research ArticleFace Recognition Incorporating Ancillary InformationSang-Ki Kim, Kar-Ann Toh, and Sangyoun LeeSchool of Electrical and Electronic Engine[r]

For psychophysics studies, our conclusions have a number of implications. First, face-recognition studies should include a range oŸ image qualities. For example, when
measuring the concord between humans and algorithms, the results should be based[r]

Copyright © 2008 Baochang Zhang et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.1. INTRODUCTIONA good object representation or pa[r]

DWT/HMM 1.09 1.19DWT/SHMM 4.31 3.454.3. Face identification results using wavelet/SHMMThe next set of experiments was designed to establish ifSHMM provided a benefit over HMM for face recogni-tion. Where appropriate, the same parameters were used forSHMM as for HMM (such as block size).[r]

ing how to combine them in optimized way by checking qua-siview size. Ongoing research includes new feature extractionmethods for profile views and missing view interpolation inthe registration step.REFERENCES[1] A. Samal and P. A. Iyengar, “Automatic recognition and anal-ysis of human faces a[r]

Hindawi Publishing CorporationEURASIP Journal on Applied Signal ProcessingVolume 2006, Article ID 30274, Pages 1–11DOI 10.1155/ASP/2006/30274Information Theory for Gabor Feature Selection forFace RecognitionLinlin Shen and Li BaiSchool of Computer Sc ience and Information Technology, The University[r]

A plethora of approaches has been proposed and evalua-tion standards have been defined, but current solutions stillneed to be improved in order to cope with the recognitionrates and robustness requirements of commercial products.Anumberofrecentsurveys[1, 2] review modern trends inthis area of researc[r]

space blindly in real applications such as face recognitionbecause of the very high dimensionality of the original data.Recently, some researchers [18–32] have explored theuse of evolutionary computation (EC) methods [28]forfeature selection and extraction. In these methods, thesolution space[r]

when both the template g and control factor k are known.Among various biometrics, face recognition has beenone of the most passive, natural, and noninvasive types ofbiometrics. Such characteristics of face recognition makeit a good choice for some surveillance and monitor[r]

Feng G.C., Yuen P.C., Dai D.Q., Human Face Recognition Using PCA on Wavelet Subband, Journal of Electronic Imaging, Vol. 9, No. 2, April 2000, pp. 226-233 Fonseca, P.; Nesvadha, J., Face detection in the compressed domain, Proc. of the 2004 International Conference on Image Proc[r]

The mathematical sophistication of HMMs combined with their successful application to a wide range of speech processing problems has prompted researchers in pattern recognition to consider their use in other areas, such as character recognition, keyword spotting, lip- Reviews, Refineme[r]

is very close to the camera. The results of face segmentationwith pose variations are shown in Figure 14.5. FEATURE EXTRACTIONAfter the face is segmented, features are extracted. Principalcomponent analysis (PCA) is a standard technique used toapproximate the original data with lower d[r]