FACE RECOGNITION AND EXPRESSION

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[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2009, Article ID 465193, 12 pagesdoi:10.1155/2009/465193Research ArticleEvolutionary Discriminant Feature Extraction withApplication to Face RecognitionQijun Zhao,1David Zhang,1Lei Zhang,1and Hongtao L[r]

By comparison, we also evaluated the magnitude of theinitial eigenvectors for the eigenface method when using theline-fit method described in Section 5. The cumulative eigen-values computed by using the β axis as the new image planeare shown in Ta ble 3 and exhibit a similar increase in in-for[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]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2008, Article ID 185281, 13 pagesdoi:10.1155/2008/185281Research ArticleComplex Wavelet Transform-Based Face RecognitionAlaa Eleyan, H¨useyin¨Ozkaramanli, and Hasan DemirelElectrical & Electron[r]

presents how to generate the novel image of the chosen sub-ject at the same pose as the test image for visual comparison.The system performance is demonstrated in Section 5.Weconclude our paper in Section 6.2. RELATED WORKAs pointed out in [1] and many references cited therein,pose and[r]

and vertical rotation from −30◦to 30◦. The resulting face descriptor basedon multiple representative views, which is of compact size, shows reasonable face recognition performance on any view. Hence,our face descriptor contains quite enough 3D information of a pers[r]

Figure 2: Ideal factor-specific submanifolds in an entire manifoldon which face images lie. Each red cur ve connects face imagesonly due to vary ing viewpoint while each blue curve connects faceimages only due to varying illumination.MPCA’s averaging is premised on the assumption that t[r]

pairwise neural networks are shown to outperform the mul-ticlass neural-network systems in terms of the predictive ac-curacy on the real face image datasets.Further in Section 2, we briefly describe a face image rep-resentation technique and then illustrate problems caused bynois[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 Ele[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 a[r]

So far, biometric identification in general and facial recognition in particular are still being researched and developed for applying in several areas such as security, etc. In this paper, the authors study on some facial image recognition methods that have been researched and published in the worl[r]

−1sc − μs. (4)(a) (b)(c) (d)Figure 1: (a) Face segmented using skin colour regions (b) full face(c) closely cropped face (d) faces of various shapes.Skin pixel classification may give rise to some false detectionof nonskin tone pixels, which should be eliminated. A, iter-ation[r]

Except where clearly acknowledged in footnotes, quotations and the bibliography, Icertify that I am the sole author of the thesis submitted today entitled Face Recognition Using Local Patterns and Relation LearningI further certify that to the best of my knowledge the the[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 surveillan[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 experime[r]

Face recognition research star ted in the late 70s and has be-come one of the active and exciting research areas in com-puter science and information technology areas since 1990.Basically, there are two major approaches in automatic recog-nition of faces by compute[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, T[r]

This presents a great challenge to face recognition. Two is-sues are central, the first is what features to use to representaface. A face image subjects to changes in viewpoint, illumi-nation, and expression. An effective representation shouldbe able to deal with po[r]

mìning whether or not the face in a probe is oŸ a person in the gallery. Identification
is the task of determining which individual is the best match to the probe. Note that
identification can be performed regardless of whether or not a face

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