An Improvement of AdaBoost for Face-Detection with Motion and Color Information

Vincenzo,; Lisá,

doi:10.1109/iciap.2007.4362830

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…일반적으로 얼굴 검출을 위하 여 AdaBoost 알고리즘을 사용할 수 있다 [4,5] . 그러나 AdaBoost 방법은 얼굴이 아닌 영역을 다수 포함하여 오류(false alarm)을 제거하기 위한 방법이 연구되어 왔다 [6][7][8] .…”

Section:      에 대해서 가중치      unclassified

Improved face detection method at a distance with skin-color and variable edge-mask filtering

Lee¹,

Yeom²,

Kim³

2012

The Journal of Korea Information and Communications Society

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Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

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Section:      에 대해서 가중치      unclassified

Improved face detection method at a distance with skin-color and variable edge-mask filtering

Lee¹,

Yeom²,

Kim³

2012

The Journal of Korea Information and Communications Society

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show abstract

“…The linear or phase-only matched filter 6 provides the candidate windows with major output peaks. The sequential testing stages comprise a skin-color test 7,8 and an edge mask filter 9 . The former utilizes the color information, and the latter considers the contour shape of the face.…”

Section: Introductionmentioning

confidence: 99%

Face identification with frequency domain matched filtering in mobile environments

et al. 2012

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Face identification at a distance is very challenging since captured images are often degraded by blur and noise. Furthermore, the computational resources and memory are often limited in the mobile environments. Thus, it is very challenging to develop a real-time face identification system on the mobile device. This paper discusses face identification based on frequency domain matched filtering in the mobile environments. Face identification is performed by the linear or phase-only matched filter and sequential verification stages. The candidate window regions are decided by the major peaks of the linear or phase-only matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering test, which verify color and shape information of the candidate regions in order to remove false alarms. All algorithms are built on the mobile device using Android platform. The preliminary results show that face identification of East Asian people can be performed successfully in the mobile environments.

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“…After the seminal work of Viola and Jones on real time face detection using AdaBoost algorithm [1], [2], the face detection problem has been well explored by many other researchers as well [3], [4], [5], [6], [7]. In all of these techniques, fast speedup have been obtained by exploiting the fixed pattern of a human face.…”

Section: Introductionmentioning

confidence: 99%

Early terminating algorithms for Adaboost based detectors

Mahmood

Khan

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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In this paper we propose an early termination algorithm for speeding up the detection phase of the Adaboost based detectors. In the basic algorithm, at a specific search location, the AdaBoost ensemble response is computed as monotonic decreasing function of weak learners. As more weak learners are evaluated, the response either decreases or remains the same. As soon as the current response becomes lower than the AdaBoost global threshold, remaining computations may be skipped without any loss of accuracy. We further extend the basic algorithm by integrating it with the Non Maxima Suppression (NMS) process. Any candidate location may be discarded, as soon as its current response becomes lower than another candidate location, within the same non-maxima suppression window.In our experiments, our proposed algorithm has been found to be an order of magnitude faster than the traditionally used AdaBoost detector, for the application of edge-corner detection. Speedup comparisons are also done with other three well known edge corner detectors. The early terminated AdaBoost detector has been found to be significantly faster than all three of these detectors.

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An Improvement of AdaBoost for Face-Detection with Motion and Color Information

Cited by 11 publications

References 5 publications

Improved face detection method at a distance with skin-color and variable edge-mask filtering

Improved face detection method at a distance with skin-color and variable edge-mask filtering

Face identification with frequency domain matched filtering in mobile environments

Early terminating algorithms for Adaboost based detectors

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