Face Verification With Balanced Thresholds

Yan, Shuicheng; Xu, Dong; Tang, Xiaoou

doi:10.1109/tip.2006.884939

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Many different projections have been proposed including famous examples like Eigenfaces [4], where the basis of the face space is spanned by eigenvectors obtained from a PCA analysis of the training images, and Fisherfaces [7], where the projection bases on Fisher's Linear Discriminant and is computed to be nearly orthogonal to intra-class scatter to minimize intra-class variance while maximizing interclass variance. Further projection methods construct linear subspaces for each person based on the original gallery images and measure the distance of query faces to these spaces [12], enhance the projection by kernel methods to model higher-order pixel dependencies [14], or refine the Fisherfaces approach with more intricate target subspaces [13], [15]. All projection methods are characterized by a low computational complexity and all modern variants and Fisherfaces attain high recognition rates on standard databases.…”

Section: Related Workmentioning

confidence: 99%

Person recognition for service robotics applications

Bormann

Zwolfer

Fischer

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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The acceptance of service robots comes along with the ability to adapt to user specific preferences. This requires that a robot can determine the identity of the user. As for humans, robust user recognition is based on the identification of the face. However, despite the plethora of published work on face recognition that is robust against real world noise such as illumination, head alignment or facial expressions there is no robust off-the-shelf non-commercial software available to be used in typical robotics applications. Hence, this paper introduces a ready-to-use open-source ROS package providing a face detection and identification system that is comprising novel and state-of-the-art solutions to various aspects of face recognition while utilizing modern RGB-D sensors. This work demonstrates a solution for face recognition in robotic settings that is robust against varying illumination, gaze directions of the head, and facial expressions while operating with online performance. The paper provides a thorough evaluation of the face recognition system based on standard database tests and on real world scenarios regarding these criteria. The work described in this project was partially funded by the European project ACCOMPANY (Acceptable robotics COMPanions for AgeiNg Years). Grant agreement no.: 28762

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Section: Related Workmentioning

confidence: 99%

Person recognition for service robotics applications

Bormann

Zwolfer

Fischer

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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“…The results of the localization can be used for the human-machine interface [2,13,14,21], the recognition of a face and facial expression [6,15,17,23,24], 3D face recognition [1], and etc. Facial feature location methods can be classified into the following two categories: (1) local methods [7,16], which detect eye, mouth, chin separately; (2) global methods [3,4,22], which detect facial features jointly.…”

Section: Introductionmentioning

confidence: 99%

Facial feature localization based on an improved active shape model

Zheng

Jia

Duanmu

et al. 2008

Information Sciences

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“…Subspace face recognition can be formulated as global formulation or class-specific formulation. In global formulation, one unified projection matrix is trained for all the classes through constructing a pooled covariance matrix [116]. In class-specific formulation, a classspecific projection matrix is trained for each class [117].…”

Section: Face Verificationmentioning

confidence: 99%

Visual recognition by subspace approaches on LBP features

Ren¹

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Subspace approaches have been widely used in visual recognition. Traditionally, they are applied on holistic features derived by vectorizing raw-image pixels. However, the performance is often limited by rigorous image alignment and high computational cost. Recently, local binary pattern (LBP) becomes popular because of robustness to illumination variations and alignment error, and fast and easy feature extraction. However, it still has some limitations such as sensitivity to image noise, high feature dimensionality, unreliable estimation of LBP histogram and non-Euclidean feature space. Subspace approaches have been applied on LBP feature and partially resolved the problems. However, non-Gaussian distribution of LBP feature limits the performance gain brought by subspace approaches. In this thesis, we exploit the advantages of both subspace approaches and LBP feature. In Chapter 3, we enhance the performance of subspace approaches on holistic features, i.e. we propose a fast and accurate subspace face/eye detector. Then, we boost the performance of LBP feature by a noise-resistant LBP with an embedded error-correction mechanism in Chapter 4, and LBP structure optimization in Chapter 5 that better captures image characteristic. Finally, we improve the performance of subspace approaches on LBP features by two learning-based LBP features in Chapter 6, and a Chi-squared transformation in Chapter 7 that tackles the non-Gaussian distribution of LBP feature. These contributions are discussed in detail as follow. Firstly, we improve the subspace approaches on holistic features. We propose a natural and non-intrusive way to secure mobile devices, i.e. a complete and fully automated face verification iv Abstract system. The proposed subspace face/eye detector locates the eyes at a much higher precision than Adaboost face/eye detector at a comparable speed thanks to discriminative APCDA features and attentional cascade strategy. The proposed approach that determines the class-specific threshold without sacrificing training data for validation data further boosts the performance. The proposed system is systematically evaluated on O2FN, AR and CAS-PEAL databases, and achieves a better recognition performance at a comparable speed compared with other approaches. Secondly, we improve the noise robustness of LBP feature. LBP feature is sensitive to noise. Local ternary pattern (LTP) partially solves the problem. However, both LBP and LTP treat the corrupted patterns as they are. Thus, we propose a noise-resistant LBP to preserve the image local structures in presence of noise. We encode the small pixel difference as an uncertain bit first, and then assign its value to form possible uniform codes that represent image micro-structures. In addition, we find that some image patterns such as lines are not captured in uniform codes. We then propose an extended noise-resistant LBP to capture line patterns. Both proposed approaches are shown more resistant to noise than other LBP variants. Thirdly, we propose to optimize the LBP struct...

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Face Verification With Balanced Thresholds

Cited by 10 publications

References 26 publications

Person recognition for service robotics applications

Person recognition for service robotics applications

Facial feature localization based on an improved active shape model

Visual recognition by subspace approaches on LBP features

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