1,248 research outputs found

    Multi-View Face Recognition From Single RGBD Models of the Faces

    Get PDF
    This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

    Matching Image Sets via Adaptive Multi Convex Hull

    Get PDF
    Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

    Learning Robust and Discriminative Manifold Representations for Pattern Recognition

    Get PDF
    Face and object recognition find applications in domains such as biometrics, surveillance and human computer interaction. An important component in any recognition pipeline is to learn pertinent image representations that will help the system to discriminate one image class from another. These representations enable the system to learn a discriminative function that can classify a wide range of images. In practical situations, the images acquired are often corrupted with occlusions and noise. Thus, a robust and discriminative learning is necessary for good classification performance. This thesis explores two scenarios where robust and discriminative manifold representations help recognize face and object images. On one hand learning robust manifold projections enables the system to adapt to images across different domains including cases with noise and occlusions. And on the other hand learning discriminative manifold representations aid in image set comparison. The first contribution of this thesis is a robust approach to visual domain adaptation by learning a subspace with L1 principal component analysis (PCA) and L1 Grassmannian with applications to object and face recognition. Mapping data from different domains on a low dimensional subspace through PCA is a common step in subspace based unsupervised domain adaptation. Subspaces extracted by PCA are prone to be affected by outliers that lead to noisy projections. A robust subspace learning through L1-PCA helps in improving performance. The proposed approach was tested on the office, Caltech - 256, Yale-A and AT&T datasets. Results indicate the improvement of classification accuracy for face and object recognition task. The second contribution of this thesis is a biologically motivated manifold learning framework for image set classification by independent component analysis (ICA) for Grassmann manifolds. It has been discovered that the simple cells in the visual cortex learn spatially localized image representations. Similar representations can be learnt using ICA. Motivated by the manifold hypothesis, a Grassmann manifold is learnt using the independent components which enables compact representation through linear subspaces. The efficacy of the proposed approach is demonstrated for image set classification on face and object recognition datasets such as AT&T, extended Yale, labelled faces in the wild and ETH - 80

    A framework of face recognition with set of testing images

    Get PDF
    We propose a novel framework to solve the face recognition problem base on set of testing images. Our framework can handle the case that no pose overlap between training set and query set. The main techniques used in this framework are manifold alignment, face normalization and discriminant learning. Experiments on different databases show our system outperforms some state of the art methods
    • …
    corecore