Multi-pose Face Detection Using Facial Features and AdaBoost Algorithm

Ruan, Jinxin; Yin, Jingwei

doi:10.1109/wcse.2009.760

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…Selain meneliti cara identifikasi tanda vital tubuh manusia, banyak peneliti yang mengembangkan metode identifikasi warna kulit manusia berdasarkan ekstraksi ruang warna RGb, YCbCr, dan lainnya. Menurut penelitian yang dilakukan oleh Jinxin Ruan, dkk [13] Pada tahun 2015, denny dkk [3] melakukan penelitian terkait dengan penapisan konten negatif pada video. Adapun metode yang dilakukan adalah mengekstraksi video menjadi frame-frame citra digital sebanyak 256 frame dengan interval waktu yang merata.…”

Section: Studi Pustakaunclassified

“…Sebagaimana telah dijelaskan pada bab II, ruang warna YCbCr dipilih untuk mendeteksi warna kulit tubuh manusia dikarenakan ruang warna ini mampu mengeliminasi iluminasi yang ditimbukan oleh efek cahaya kamera maupun tv [13]. Citra objek kandidat puting tersebut dikonversi dari ruang warna RGB ke ruang warna YCbCr dengan rumus ditunjukkan pada Persamaan (22) [5]:…”

Section: ) Ekstraksi Fitur Ruang Warna Ycbcrunclassified

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Identifikasi Konten Negatif pada Citra Digital Berbasis Tanda Vital Tubuh Menggunakan Ekstraksi Fitur GLCM dan Warna YCbCr

Hardiyanto

Sartika

2017

JIS

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Currently, the rapid development of information technology causes various positive impacts and negative impacts. All information (positive and negative contents) are available on the internet. They can easily accessible by various community members including students. Negative content or pornography contained in the internet can have adverse effects, affect the psychological and mental state, especially among students. The purpose of this research is to develop a system for identifying negative content based on the detection of the body's vital signs. The object of the body's vital signs is the nipple. The proposed method is a combination of face detection and face replace to reduce false positive error in the face area. Furthermore, Haar-Cascade Classifier training uses 1000 positive images data (nipple images) and 8000 negative images data (images that not contain of nipple). The feature extraction stage uses the Gray Level Co-occurrence Matrix (GLCM) 84 attribute and the result is continued YCbCr color space feature extraction process. The classification process use Multi Layer Perceptron with architecture of 10 neurons and 1 hidden layer. By using 158 data of nipple candidate objects, this research was able to detect nipple content with accuracy value of 90,3%, specificity value of 84,60%, and sensitivity value of 92,4%. This is shows that the addition of YCbCr color space feature extraction can increase the accuracy value of 0.9% and the sensitivity value of 1.04%.

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Section: Studi Pustakaunclassified

Section: ) Ekstraksi Fitur Ruang Warna Ycbcrunclassified

Identifikasi Konten Negatif pada Citra Digital Berbasis Tanda Vital Tubuh Menggunakan Ekstraksi Fitur GLCM dan Warna YCbCr

Hardiyanto

Sartika

2017

JIS

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“…Finally, an area threshold and an aspect ratio are used to validate the corrected facial region. After then we use the Adaboost algorithm [7][8][9][10] to make face recognition. At last, we can judge by the distance between the captured face and advertising, when the person is closer and closer, the words will accord to the distance for scaling to achieve…”

Section: Interactivity Kanban Advertisementmentioning

confidence: 99%

“…Face Detection this issue in order to obtain better characteristics also introduces a machine learning concepts, these studies are a breakthrough in the past to the face detection frame, most notably the 2004 study is presented using the integral image Viola for the characteristic value of the AdaBoost face detection method. AdaBoost is an algorithm for constructing a "strong" classifier as linear combination of "weak" classifiers [7][8][9][10].…”

Section: Adaboost Algorithmmentioning

confidence: 99%

Digital Interactive Kanban Advertisement System Using Face Recognition Methodology

Cheng¹,

Chang²,

Jong³

2013

CWEEE

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Most of advertisement systems are presently still launch the publicity content by the static words and pictures. Recently, this static advertisement model will not be able to attract people's attention more and more. Moreover, the static information content of advertisement system is limited because of the layout shown size. It can not also fully demonstrate the information content of advertisement system. In this paper, we develop a digital interactive kanban advertisement system using face recognition methodology to solve these problems. The system captures the person's face through the camera. The digital advertisement content size is relevant by the person and camera observation locations. In this paper, we adopt the Adaboost algorithm to judge people face, and the system only need to grab the position of the face. The system doesn't built expensive and complex equipment to reduce the system cost and enhance the system performance. This system can also achieve the same similar digital interactive advertising effectiveness.

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“…The AdaBoost algorithm combines the learning ability of many weak classifiers to design a strong classifier. In the past years, the AdaBoost classifier has been used for two important biometric applications such as face detection [11][12][13] and facial expression recognition [14,15]. The authors in [16] have proposed a novel cascased AdaBoost classifier model for the classification of hyperspectral image data.…”

Section: Introductionmentioning

confidence: 99%

AdaBoost classifiers for phasor measurements‐based security assessment of power systems

Thirugnanasambandam

Jain

2018

IET Generation, Transmission & Distribution

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Multi-pose Face Detection Using Facial Features and AdaBoost Algorithm

Cited by 11 publications

References 7 publications

Identifikasi Konten Negatif pada Citra Digital Berbasis Tanda Vital Tubuh Menggunakan Ekstraksi Fitur GLCM dan Warna YCbCr

Identifikasi Konten Negatif pada Citra Digital Berbasis Tanda Vital Tubuh Menggunakan Ekstraksi Fitur GLCM dan Warna YCbCr

Digital Interactive Kanban Advertisement System Using Face Recognition Methodology

AdaBoost classifiers for phasor measurements‐based security assessment of power systems

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