FACE RECOGNITION WITH OPENCV

Learn How to Draw a Face with Confidence! I know when I learn to do something for the first time that it helps if I can actually interact and "do" the activity at hand. Nothing could be truer then when learning to draw, especially when learning how to draw a face. If you just s[r]

Computer Vision – Face Detection in Java with OpenCV using JavaCV(http://tkgospodinov.com/computer-vision-face-detection-in-java-with-opencv-using-javacv/)I stumbled upon a few libraries that enable us to put some artificial intelligence goodness right into[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]

the fact that face recognition is actually to find the most sim-ilar match with the least difference, the selected features willalso be very important for recognition.5. KERNEL ENHANCEMENT FOR RECOGNITIONOnce the most informative Gabor features are selected, dif-ferent appr[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2009, Article ID 260148, 16 pagesdoi:10.1155/2009/260148Research ArticleSorted In dex Numbers for Privacy Preserving Face RecognitionYongjin Wang and Dimitrios HatzinakosThe Edward S. Rogers Sr. Depar tment o[r]

parameters, where N and M are the dimensions of featuresin the cepstral domain and the log-spectral domain, respec-tively. It can be seen that the computational complexity ofthe MC-SNSC plus the conventional MC methods is com-parable to that of the conventional MC methods. However,the MC-SNSC is mor[r]

How to paint a face with Photoshop

Hindawi Publishing CorporationEURASIP Journal on Image and Video ProcessingVolume 2007, Article ID 38205, 11 pagesdoi:10.1155/2007/38205Research ArticleFusion of Appearance Image and Passive Stereo Depth Map forFace Recognition Based on the Bilateral 2DLDAJian-Gang Wang,1Hui Kong,2Eric Sung,2[r]

Hindawi Publishing CorporationEURASIP Journal on Audio, Speech, and Music ProcessingVolume 2007, Article ID 64506, 9 pagesdoi:10.1155/2007/64506Research ArticleAudio-Visual Speech Recognition Using Lip InformationExtracted from Side-Face ImagesKoji Iwano, Tomoaki Yoshinaga, Satoshi Tam[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 bynoise and v[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]

Hindawi Publishing CorporationEURASIP Journal on Audio, Speech, and Music ProcessingVolume 2007, Article ID 64506, 9 pagesdoi:10.1155/2007/64506Research ArticleAudio-Visual Speech Recognition Using Lip InformationExtracted from Side-Face ImagesKoji Iwano, Tomoaki Yoshinaga, Satoshi Tam[r]

and facial expressions analysis. The final aim of [4]istode-velop human-computer interfaces that react in a similar wayto a communication between human beings. Smart roomsand ambient intelligence systems offer the possibility of mix-ing real and virtual worlds in mixed reality applications [3].People[r]

worth the extra computation because the CMD accuracy ismuch higher than others.Compared to speech recognition, the processing proce-dure of CMD and DMD in Figure 6 is similar to speechrecognition. Speech recognition first partitions the speechstream into segment sequences, usually overl[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]