Statistical Learning of Multi-view Face Detection

Li, Stan Z.; Zhu, Long; Zhang, ZhenQiu; Blake, A.; Zhang, Hong-Jiang; Shum, Harry

doi:10.1007/3-540-47979-1_5

Cited by 279 publications

(197 citation statements)

References 16 publications

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“…In this section, following [58] and [55], we briefly present a generalized version of AdaBoost algorithm, usually referred to as RealBoost. It has been advocated in various works [59,60,61,62] that RealBoost yields better performance than the original AdaBoost algorithm.…”

Section: Adaboost Learningmentioning

confidence: 99%

“…A number of researchers noted the limitation of the original Haar-like feature set in [17] for multi-view face detection, and proposed to extend the feature set by allowing a more flexible combination of rectangular regions. For instance, in [59], three types of features were defined in the detection sub-window, as shown in Fig. 6 (a).…”

Section: Feature Extractionmentioning

confidence: 99%

“…In the original face detection paper by Viola and Jones [17], the standard AdaBoost algorithm [56] was adopted. In a number of follow-up works [59,60,61,62], researchers advocated the use of RealBoost, which was explained in detail in Section 2.1.2. In [107] and [66] comparisons between three boosting algorithms: AdaBoost, RealBoost and GentleBoost were performed.…”

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

“…In [59], the FloatBoost algorithm was proposed, which attempted to overcome the monotonicity problem of the sequential AdaBoost Learning. Specifically, AdaBoost is a sequential forward search procedure using a greedy selection strategy, which may be suboptimal.…”

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

A survey on face detection in the wild: Past, present and future

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

379

167

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Section: Adaboost Learningmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

Section: Variations Of the Boosting Learning Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

A survey on face detection in the wild: Past, present and future

Zafeiriou

Cha

Zhang

2015

Computer Vision and Image Understanding

379

167

View full text Add to dashboard Cite

“…Several approaches have recently been described to tackle reliable face detection in real time (Schneiderman and Kanade, 2000;Li et al, 2002;Viola and Jones, 2004), making face detection less environment dependent. Cue combination usually provides greater robustness and higher processing speeds, particularly for live video stream processing.…”

Section: Face and Eye Detectionmentioning

confidence: 99%

Computer vision based eyewear selector

Déniz

Castrillón

Lorenzo

et al. 2010

J. Zhejiang Univ. - Sci. C

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Abstract:The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

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View‐centralized multi‐atlas classification for Alzheimer's disease diagnosis

Liu

Zhang

Shen

et al. 2015

Human Brain Mapping

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Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods.

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Statistical Learning of Multi-view Face Detection

Cited by 279 publications

References 16 publications

A survey on face detection in the wild: Past, present and future

A survey on face detection in the wild: Past, present and future

Computer vision based eyewear selector

View‐centralized multi‐atlas classification for Alzheimer's disease diagnosis

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