ILLUMINATION COMPENSATION OF FACIAL IMAGE USING COMBINATION ALGORITHM FOR FACE RECOGNITION

cach probe image p, € ?, we sort z,(-) for all gallery images ø, ở. We assume that a smaller similarity score implies a closer match. The function id(7) gives the index of
the gallery image of the person in probe Ø,, ie[r]

Clean Handset Codec Handset + CodecBaseline 99.64% 85.78% 70.94% 55.98%Cepstral meansubtraction99.31% 99.10% 84.57% 72.86%behavior with various scalar factors. The distortion is mea-sured by the average distance between LSFs of before and af-ter channel compensation. The resulted curve[r]

[6] C. Bishop, “Pattern Recognition and Machine Learning”, Springer Press (2006) [7] I. Buciu and I. Pista, “Application of non-Negative and Local non Negative Matrix Factorization to Facial Expression Recognition”, the 17th 17th International Conference on Pattern Rec[r]

academic and recreational fine art educators. 17 PAGES – 28 ILLUSTRATIONS Published by Hoddinott Fine Art Publishers, Halifax, NS, Canada – Revised 2006 Copyright to all articles, images, text, projects, lessons and exercises within this drawing class belong to Brenda Hoddinott and may not be r[r]

columns 9–12: images in CDS008(b) Normalized depth images corresponding to (a)Figure 3: Normalized 2D and 3D face images in the XM2VTS database: (a) appearance images, (b) depth images.performance of the system. The criterion for this kind ofcombination is to fully make use o[r]

EURASIP Journal on Applied Signal Processing 2004:4, 530–541c 2004 Hindawi Publishing CorporationFace Recognition Using Local and Global FeaturesJian HuangDepartment of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong KongEmail: jhuang@comp.hkbu.edu.hkPong C.[r]

to cluster the entire face with respect to some features suchas colors, textures, and shapes. These well-organized clus-ters sensed by the human’s brain are the facial regions suchas lips, hair, forehead, eyes, and so on. They are all com-posed of similar symbols that unfold the[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[r]

nodes under (i, j) are insignificant. It also indicates that atleast one of the four offspringsofnode(i, j) is significant. Inthis situation, four SIG(k, l) bits where (k, l)∈ O(i, j) needto be sent. Let the significance of the four offsprings of node(i, j) be referred to as “ABCD.”[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2008, Article ID 417293, 13 pagesdoi:10.1155/2008/417293Research ArticleFuzzy Image Segmentation Using Membership ConnectednessMaryam Hasanzadeh and Shohreh KasaeiComputer Engineering Department, Shari[r]

cropped and captured under well-controlled conditions. Thefirst step in an automatic face recognition system is to lo-calize the face region in a cluttered background and care-fully segment the face from each frame of a video sequence.Various methods have been propo[r]

work. T hey reported a high success r ate at most of SNRs.The authors did not clarify the correction percentage of eachof the modulations individually. Also they have used a fullyconnected neur al network. This causes a long training timeas well as the high complexity of the cla[r]

motivate us to make kernel Fisher discriminant analysisfor HLGPP (K-HLGPP). Experiments on the large-scalestandard FERET, FERET200 [16], and CAS-PEAL [17]databases are performed to evaluate the effectiveness ofHLGPP and K-HLGPP methods. Experimental results showthat the proposed methods are much bett[r]

design. The effect of the nonlinear processing is to thin the blurred image edges and to drive the overall blind restoration algorithmto a sharp, focused image. The performance of the algorithm is assessed by experimental results pertaining to restoration of[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]

against a dark homogeneous background with the subjectsin an up-right, frontal position, with tolerance for some sidemovements. There is also some variations in scale.There are several approaches for classification of theORL database images. In [14], a hidden Markov model(HMM) ba[r]

can b e used as a key preprocessing step for face recognitionbut also has its own importance in several applications, suchas tracking, video indexing, and so on. In general, the facedetection problem is known to be very difficult due to thevariations in race, gender, pose, expressions, a[r]

long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously beenapplied to the problem of face identification. The results reported in this application have shown that SHMM outperforms thetraditional hidden Markov model with a 73% i[r]

the image of a “top model” is considered, a good image mayhide some details like, for example, no perfect skin. For a sur-gical endoscope a good image is one showing the organ ofinterest in all details clearly for human interpretation. How-ever, in th[r]

(b) ROC for AHMFigure 7: ROC of FAR versus FRR for MOBIO Dev Set for MSM and AHM.12 EURASIP Journal on Image and Video Processingresults show that MRH feature is more discriminative forface recognition with illumination, pose, and expressionvariations[r]