Recognising Trajectories of Facial Identities Using Kernel Discriminant Analysis

Li, Yongmin; Gong, Shaogang; Liddell, Heather M.

doi:10.5244/c.15.63

Cited by 22 publications

(17 citation statements)

References 17 publications

(7 reference statements)

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“…Better results obtained by kernel subspace methods have been reported over conventional subspace methods in various vision applications (e.g. [10][11][12]). One of the most relevant work to ours is in [12] where the authors explore the application of KDA to frontal face detection and promising experimental results are reported.…”

Section: Introductionmentioning

confidence: 99%

Visual tracking via efficient kernel discriminant subspace learning

Shen

Hengel

Brooks

2005

IEEE International Conference on Image Processing 2005

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Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning strategy to find a discriminative model which distinguishes the tracked object from the background. Principal Component Analysis and Linear Discriminant Analysis have been applied to this problem with some success. These techniques are limited, however, by the fact that they are capable only of identifying linear subspaces within the data. Kernel based methods, in contrast, are able to extract nonlinear subspaces, and thus represent more complex characteristics of the tracked object and background. This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur. An efficient approximation to Kernel Discriminant Analysis using QR decomposition proposed by Xiong et al.[1] makes possible realtime updating of the optimal nonlinear subspace. We present a tracking method based on this result and show promising experimental results on real videos undergoing large pose and illumination changes.

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Section: Introductionmentioning

confidence: 99%

Visual tracking via efficient kernel discriminant subspace learning

Shen

Hengel

Brooks

2005

IEEE International Conference on Image Processing 2005

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“…Li et al [74,73,72,75] proposed to model facial dynamics by constructing facial identity structures across views and over time, referred to identity surfaces (shown in Fig. 3), in the Kernel Discriminant Analysis feature space.…”

Section: Temporal Model Based Approachesmentioning

confidence: 99%

“…Method Key-frame based Approaches [90], [40], [47], [114], [100], [17], [115], [31], [78], [85], [98], [101], [118] Temporal Model based Approaches [74], [73], [72], [75], [18], [24], [67], [69], [68], [122], [120], [123], [121], [64], [65], [66], [79], [55], [2], [43], [50], [49] Image-Set Matching based Approaches Statistical model-based [93], [4], [96], [7], [10], [6], [9] Mutual subspace-based [110], [90], [35], [82], [108], [56], [57], [5]<...>…”

Section: Categorymentioning

confidence: 99%

Face Recognition and Retrieval in Video

Shan¹

2010

Video Search and Mining

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Abstract. Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.

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“…2 outline this algorithm. To get a comprehensive view of the underlying data structure, we study four popular subspaces so that the best subspace descriptors can be found: Principle Component Analysis (PCA) [18]; Kernel Principle Component Analysis (KPCA) [19]; Multiple class Discriminant Analysis (MDA) [18] and Kernel Discriminant Analysis (KDA) [20,21]. Results show that analysis in the kernel space can provide a better performance.…”

Section: Algorithm Frameworkmentioning

confidence: 99%

“…This inspires the use of kernel machine, which explores the non-linearity of the data space. The extended nonlinear alternative, KPCA [19,23] and KDA [20], are used.…”

Section: Subspace Projectionmentioning

confidence: 99%

An Integrated Two-Stage Framework for Robust Head Pose Estimation

Trivedi

2005

Lecture Notes in Computer Science

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Abstract. Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated by analyzing finer geometrical structure details captured by bunch graphs. This coarse-tofine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from −90 o to 90 o and the tilt angle spanning from −60 o to 45 o . The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.

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Recognising Trajectories of Facial Identities Using Kernel Discriminant Analysis

Cited by 22 publications

References 17 publications

Visual tracking via efficient kernel discriminant subspace learning

Visual tracking via efficient kernel discriminant subspace learning

Face Recognition and Retrieval in Video

An Integrated Two-Stage Framework for Robust Head Pose Estimation

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