Cascaded Face Detection Using Neural Network Ensembles

Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classif...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zuo, Fei [verfasserIn] de With, Peter H. N. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Schlagwörter:	Detection Efficiency Detection Task Detection Accuracy Face Detection Homogeneous Structure

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2008(2007), 1 vom: 24. Okt.
Übergeordnetes Werk:	volume:2008 ; year:2007 ; number:1 ; day:24 ; month:10

Links:	Volltext

DOI / URN:	10.1155/2008/736508

Katalog-ID:	SPR031987958

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allfields	10.1155/2008/736508 doi (DE-627)SPR031987958 (SPR)736508-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Zuo, Fei verfasserin aut Cascaded Face Detection Using Neural Network Ensembles 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost. Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213 de With, Peter H. N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2008(2007), 1 vom: 24. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2008 year:2007 number:1 day:24 month:10 https://dx.doi.org/10.1155/2008/736508 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2008 2007 1 24 10
spelling	10.1155/2008/736508 doi (DE-627)SPR031987958 (SPR)736508-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Zuo, Fei verfasserin aut Cascaded Face Detection Using Neural Network Ensembles 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost. Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213 de With, Peter H. N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2008(2007), 1 vom: 24. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2008 year:2007 number:1 day:24 month:10 https://dx.doi.org/10.1155/2008/736508 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2008 2007 1 24 10
allfields_unstemmed	10.1155/2008/736508 doi (DE-627)SPR031987958 (SPR)736508-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Zuo, Fei verfasserin aut Cascaded Face Detection Using Neural Network Ensembles 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost. Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213 de With, Peter H. N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2008(2007), 1 vom: 24. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2008 year:2007 number:1 day:24 month:10 https://dx.doi.org/10.1155/2008/736508 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2008 2007 1 24 10
allfieldsGer	10.1155/2008/736508 doi (DE-627)SPR031987958 (SPR)736508-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Zuo, Fei verfasserin aut Cascaded Face Detection Using Neural Network Ensembles 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost. Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213 de With, Peter H. N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2008(2007), 1 vom: 24. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2008 year:2007 number:1 day:24 month:10 https://dx.doi.org/10.1155/2008/736508 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2008 2007 1 24 10
allfieldsSound	10.1155/2008/736508 doi (DE-627)SPR031987958 (SPR)736508-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Zuo, Fei verfasserin aut Cascaded Face Detection Using Neural Network Ensembles 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost. Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213 de With, Peter H. N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2008(2007), 1 vom: 24. Okt. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2008 year:2007 number:1 day:24 month:10 https://dx.doi.org/10.1155/2008/736508 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2008 2007 1 24 10
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2008(2007), 1 vom: 24. Okt. volume:2008 year:2007 number:1 day:24 month:10
sourceStr	Enthalten in EURASIP journal on advances in signal processing 2008(2007), 1 vom: 24. Okt. volume:2008 year:2007 number:1 day:24 month:10
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institution	findex.gbv.de
topic_facet	Detection Efficiency Detection Task Detection Accuracy Face Detection Homogeneous Structure
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container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Zuo, Fei @@aut@@ de With, Peter H. N. @@aut@@
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author	Zuo, Fei
spellingShingle	Zuo, Fei ddc 620 bkl 53.73 misc Detection Efficiency misc Detection Task misc Detection Accuracy misc Face Detection misc Homogeneous Structure Cascaded Face Detection Using Neural Network Ensembles
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topic_title	620 ASE 53.73 bkl Cascaded Face Detection Using Neural Network Ensembles Detection Efficiency (dpeaa)DE-He213 Detection Task (dpeaa)DE-He213 Detection Accuracy (dpeaa)DE-He213 Face Detection (dpeaa)DE-He213 Homogeneous Structure (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Detection Efficiency misc Detection Task misc Detection Accuracy misc Face Detection misc Homogeneous Structure
topic_unstemmed	ddc 620 bkl 53.73 misc Detection Efficiency misc Detection Task misc Detection Accuracy misc Face Detection misc Homogeneous Structure
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title	Cascaded Face Detection Using Neural Network Ensembles
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title_full	Cascaded Face Detection Using Neural Network Ensembles
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title_sort	cascaded face detection using neural network ensembles
title_auth	Cascaded Face Detection Using Neural Network Ensembles
abstract	Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost.
abstractGer	Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost.
abstract_unstemmed	Abstract We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost.
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title_short	Cascaded Face Detection Using Neural Network Ensembles
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