IEEE Trans Pattern Anal Mach Intell - Transform-Invariant PCA: A Unified Approach to Fully Automatic Face Alignment, Representation, and Recognition.

Tópicos

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Resumo

We develop a Transform-Invariant PCA (TIPCA) technique which aims to accurately characterize the intrinsic structures of the human face that are invariant to the in-plane transformations of the training images. Specially, TIPCA alternately aligns the image ensemble and creates the optimal eigenspace with the objective to minimize the mean square error between the aligned images and their reconstructions. The learning from the FERET facial image ensemble of 1196 subjects validates the mutual promotion between image alignment and eigenspace representation, which eventually leads to the optimized coding and recognition performance that surpasses the manual alignment based approaches. Experimental results also suggest that state-of-the-art invariant descriptors, such as local binary pattern (LBP), histogram of oriented gradient (HOG), and gabor energy filter (GEF), and classification methods, such as sparse representation based classification (SRC) and support vector machine (SVM), can benefit from using the TIPCA-aligned faces, instead of the manually eye-aligned faces that are widely regarded as the ground-truth alignment. Favorable accuracies against the state-of-the-art results on face coding and face recognition are reported.

Resumo Limpo

develop transforminvari pca tipca techniqu aim accur character intrins structur human face invari inplan transform train imag special tipca altern align imag ensembl creat optim eigenspac object minim mean squar error align imag reconstruct learn feret facial imag ensembl subject valid mutual promot imag align eigenspac represent eventu lead optim code recognit perform surpass manual align base approach experiment result also suggest stateoftheart invari descriptor local binari pattern lbp histogram orient gradient hog gabor energi filter gef classif method spars represent base classif src support vector machin svm can benefit use tipcaalign face instead manual eyealign face wide regard groundtruth align favor accuraci stateoftheart result face code face recognit report

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