Titlebook: Biometric Recognition; 6th Chinese Conferen Zhenan Sun,Jianhuang Lai,Tieniu Tan Conference proceedings 2011 The Editor(s) (if applicable) a

否認(rèn)

豐滿有漂亮

Color Face Recognition Based on Statistically Orthogonal Analysis of Projection Transforms from three color component image sets. Experimental results on the AR and FRGC version 2 color face image databases demonstrate that SOA achieves better recognition results than several related color face recognition methods.

Indent

Real-Time Head Pose Estimation Using Random Regression Foreststs are employed to map discriminative integral slice features to continuous head poses, given the advantage that they can maintain accuracy when a large proportion of the data is missing. Experimental results on the ETH database demonstrate that the proposed method is more accurate than state-of-the-art methods for head pose estimation.

四溢

Increment

Iterative Directional Ray-Based Iris Segmentation for Challenging Periocular Imagesow resolution. Experiments on the Face and Ocular Challenge Series (FOCS) database from the U.S.?National Institute of Standards and Technology (NIST) emphasize the pros and cons of the proposed segmentation algorithm.

Biofeedback

Coronation

Palm Vein Recognition Based on Three Local Invariant Feature Extraction Algorithmsein recognition. First, the images were preprocessed through histogram equalization, then three algorithms were used to extract local features, and finally the results were obtained by comparing the Euclidean distance. Experiments show that they achieve good performances on our own database and PolyU multispectral palmprint database.

珠寶

仔細(xì)檢查

0302-9743 ese Conference on Biometric Recognition, CCBR 2011, held in Beijing, China in December 2011. The 35 revised full papers were carefully reviewed and selected from 71 submissions. The papers are organized in topical sections on problems in face; iris; hand biometrics; speaker; handwriting; gait; behav

協(xié)奏曲

https://doi.org/10.1057/9780230523883f facial images, illumination changes, pose variations and occlusions. Related to applications, we divide the existing video based face recognition approaches into two categories: video-image based methods and video-video based methods, which are surveyed and analyzed in this paper.