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A novel two-stage illumination estimation framework for expression recognition.

ABSTRACT: One of the critical issues for facial expression recognition is to eliminate the negative effect caused by variant poses and illuminations. In this paper a two-stage illumination estimation framework is proposed based on three-dimensional representative face and clustering, which can estimate illumination directions under a series of poses. First, 256 training 3D face models are adaptively categorized into a certain amount of facial structure types by k-means clustering to group people with similar facial appearance into clusters. Then the representative face of each cluster is generated to represent the facial appearance type of that cluster. Our training set is obtained by rotating all representative faces to a certain pose, illuminating them with a series of different illumination conditions, and then projecting them into two-dimensional images. Finally the saltire-over-cross feature is selected to train a group of SVM classifiers and satisfactory performance is achieved when estimating a number of test sets including images generated from 64 3D face models kept for testing, CAS-PEAL face database, CMU PIE database, and a small test set created by ourselves. Compared with other related works, our method is subject independent and has less computational complexity O(C × N) without 3D facial reconstruction.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC4009132 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:In microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for individual association, followed by the Benjamini-Hochberg (BH) procedure to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. The taxonomic tree of microbiome data represents alignment from phylum to species rank and characterizes evolutionary relationships across microbial taxa. Taxa that are closer on the tree usually have similar responses to the exposure (environment). The statistical power in microbial association tests can be enhanced by efficiently employing the prior evolutionary information via the taxonomic tree. METHODS:We propose a two-stage microbial association mapping framework (massMap) which uses grouping information from the taxonomic tree to strengthen statistical power in association tests at the target rank. massMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. The method then proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH (HBH) and selected subset testing (SST) procedures are evaluated to control the FDR for the two-stage structured tests. RESULTS:Our simulations show that massMap incorporating OMiAT and the advanced FDR controlling methodologies largely alleviates the multiplicity issue. It is statistically more powerful than the traditional association mapping directly at the target rank while controlling the FDR at desired levels under most scenarios. In our real data analyses, massMap detects more or the same amount of associated species with smaller adjusted p values compared to the traditional method, which further illustrates the efficiency of the proposed framework. The R package of massMap is publicly available at https://sites.google.com/site/huilinli09/software and https://github.com/JiyuanHu/ . CONCLUSIONS:massMap is a novel microbial association mapping framework and achieves additional efficiency by utilizing the intrinsic taxonomic structure of microbiome data.

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A novel two-stage illumination estimation framework for expression recognition.

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