000 04473nam a2200625 i 4500
001 6813296
003 IEEE
005 20200413152859.0
006 m eo d
007 cr cn |||m|||a
008 101013s2010 caua foab 000 0 eng d
020 _a9781608451340 (electronic bk.)
020 _z9781608451333 (pbk.)
024 7 _a10.2200/S00300ED1V01Y201009COV002
_2doi
035 _a(CaBNVSL)gtp00544194
035 _a(OCoLC)664597169
040 _aCaBNVSL
_cCaBNVSL
_dCaBNVSL
050 4 _aTA1650
_b.Z423 2010
082 0 4 _a006.42
_222
100 1 _aZhang, Cha.
245 1 0 _aBoosting-based face detection and adaptation
_h[electronic resource] /
_cCha Zhang and Zhengyou Zhang.
260 _aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_cc2010.
300 _a1 electronic text (x, 128 p. : ill.) :
_bdigital file.
490 1 _aSynthesis lectures on computer vision,
_x2153-1064 ;
_v# 2
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (p. 113-126).
505 0 _a1. A brief survey of the face detection literature -- Introduction -- The Viola-Jones face detector -- The integral image -- AdaBoost learning -- The attentional cascade structure -- Recent advances in face detection -- Feature extraction -- Variations of the boosting learning algorithm -- Other learning schemes -- Book overview --
505 0 _a2. Cascade-based real-time face detection -- Soft-cascade training -- Fat stumps -- Multiple instance pruning -- Pruning using the final classification -- Multiple instance pruning -- Experimental results --
505 0 _a3. Multiple instance learning for face detection -- MILboost -- Noisy-or MILboost -- ISR MILboost -- Application of MILboost to low resolution face detection -- Multiple category boosting -- Probabilistic McBoost -- Winner-take-all McBoost -- Experimental results -- A practical multi-view face detector --
505 0 _a4. Detector adaptation -- Problem formulation -- Parametric learning -- Detector adaptation -- Taylor-expansion-based adaptation -- Adaptation of logistic regression classifiers -- Logistic regression -- Adaptation of logistic regression classifier -- Direct labels -- Similarity labels -- Adaptation of boosting classifiers -- Discussions and related work -- Experimental results -- Results on direct labels -- Results on similarity labels --
505 0 _a5. Other applications -- Face verification with boosted multi-task learning -- Introduction -- AdaBoosting LBP -- Boosted multi-task learning -- Experimental results -- Boosting-based multimodal speaker detection -- Introduction -- Related works -- Sound source localization -- Boosting-based multimodal speaker detection -- Merge of detected windows -- Alternative speaker detection algorithms -- Experimental results --
505 0 _a6. Conclusions and future work -- Bibliography -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aFace detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms.We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning.
530 _aAlso available in print.
588 _aTitle from PDF t.p. (viewed on October 13, 2010).
650 0 _aHuman face recognition (Computer science)
_xMathematical models.
653 _aface detection
653 _aboosting
653 _amultiple instance learning
653 _aadaptation
653 _amultiple task learning
653 _amultimodal fusion
700 1 _aZhang, Zhengyou,
_d1965-
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on computer vision,
_x2153-1064 ;
_v# 2.
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813296
999 _c561783
_d561783