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In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both...
Recent studies in patch-based Gaussian Mixture Model (GMM) approaches for face age estimation present promising results. We propose using a hidden Markov model (HMM) supervector to represent face image patches, to improve from the previous GMM supervector approach by capturing the spatial structure of human faces and loosening the assumption of identical face patch distribution within a face image...
In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local binary pattern) descriptor. Firstly we review...
In this paper, we study the problem of subspace-based face recognition under scenarios with spatial misalignments and/or image occlusions. For a given subspace, the embedding of a new datum and the underlying spatial misalignment parameters are simultaneously inferred by solving a constrained lscr1 norm optimization problem, which minimizes the error between the misalignment-amended image and the...
Subspace learning based face recognition methods have attracted considerable interests in recently years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE), marginal fisher analysis (MFA) and local discriminant embedding (LDE). These methods consider an n1timesn2 image as a vector in Rn1...
Robust face detection presents a difficult problem in real interaction scenarios, that, in order to achieve, most often requires employing additional sources of information. In this paper, we consider two such sources: temporal information, available in the form of video sequences, and spatial information, available from multiple calibrated cameras with synchronous, overlapping fields of view of the...
Estimating 3D head poses accurately in low resolution video is a challenging vision task because it is difficult to find continuous one-to-one mapping from person-independent low resolution visual representation to head pose parameters. We propose to track head poses by modeling the shape-free facial textures acquired from the video with subspace learning techniques. In particular, we propose to model...
While PCA learns a subspace that captures the variations of the data, it assumes the collected data is well pre-processed (i.e., the pictures for faces are aligned by eye corners), this usually introduces a huge mount of manual labor for human. While people have been developing automatic eye alignment tools for such purpose, detecting eyes with robustness and accuracy is still an open problem for...
A minimum variance estimation framework for 3D face reconstruction from multiple views and a new 3D surface reconstruction algorithm based on deformable subdivision mesh is proposed in this paper. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with minimum variance estimation. Then, a new...
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