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In recent years, non-rigid structure from motion (NRSFM) has become one of the hottest issues in computer vision due to its wide applications. In practice, the number of available high-quality images may be limited in many cases. Under such a condition, the performances may not be satisfactory when existing NRSFM algorithms are applied directly to estimate the 3D coordinates of a small-size image sequence. In this paper, a sub-sequence-based integrated algorithm is proposed to deal with the NRSFM problem with small sequence sizes. In the proposed method, sub-sequences are first extracted from the original sequence. In order to obtain diversified estimations, multiple weaker estimators are constructed by applying the extracted sub-sequences to a recent NRSFM algorithm with a rotation-invariant kernel (RIK). Compared to other first-order statistics, the trimmed mean is a relatively robust statistic. Considering the fact that the estimations of some weaker estimators may have large errors, the trimmed means of the outputs for all the weaker estimators are computed to determine the final estimated 3D shapes. Compared to some existing methods, the proposed algorithm can achieve a higher estimation accuracy, and has better robustness. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]