Titlebook: Biometric Recognition; 8th Chinese Conferen Zhenan Sun,Shiguan Shan,YiLong Yin Conference proceedings 2013 Springer International Publishin

GEM

Complete Pose Binary SIFT for Face Recognition with Pose Variation SIFT based face recognition schemes could resolve the problem of constrained pose variation without such preprocessing. we find that the sift descriptors are robust to off-plane rotation within 25 degree and in-plane rotation. Furthermore, we propose complete pose binary SIFT (CPBS) to address the

generic

Coupled Kernel Fisher Discriminative Analysis for Low-Resolution Face RecognitionDA) for LR face recognition. Firstly, the high-resolution (HR) and low-resolution (LR) training samples are respectively mapped into two different high-dimensional feature spaces by using kernel functions. Then CKFDA learns two mappings from the kernel images to a common subspace where discriminatio

alliance

LIEN

A Method for Efficient and Robust Facial Features Localizationture called multi-resolution wrapped features (MRWF), which is robust to scale and poses variation, and can be calculated very efficiently. The second is a new gradient boosting method based on a mixture re-sampling strategy, which allows the model to resistant to imbalance of training samples. The

Ossification

多產(chǎn)魚

Kernelized Laplacian Collaborative Representation Based Classifier for Face RecognitionC_RLS), has attracted notable attention. The extensive experiments demonstrate that the CRC_RLS technique has less complexity than traditional sparse representation based classifier (SRC) but results in better classification performance. However, the existing SRC-like approaches fail to consider the

擁護

morale

Kernel Collaborative Representation with Regularized Least Square for Face Recognitionds optimize an objective function with L1-Norm. SRC consists of two parts: collaborative representation and L1-norm constrain. Based on SRC, collaborative representation based classification with regularized least square (CRC_RLS) is prosed. CRC_RLS is a linear method in nature. There are many varia

cringe

Two-Dimensional Color Uncorrelated Principal Component Analysis for Feature Extraction with Applicatrom color face images. The 2DCUPCA can be used to explore uncorrelated properties among color-based features, which contain minimum redundancy and ensure linear independence among features. Furthermore, the proposed 2DCUPCA provided the theoretical foundations analysis and proved the uncorrelated pr

不透明性