Image skin segmentation based on multi-agent learning Bayesian and neural network

Zaidan, A. A.; Ahmad, Nazihah; Karim, Hezerul Abdul; Larbani, Moussa; Zaidan, B. B.; Sali, Aduwati

doi:10.1016/j.engappai.2014.03.002

Cited by 27 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These factors can create major difficulties in face pixel-based skin detectors when the colour feature is used (Zaidan et al 2014). Thus, Zaidan et al (2014) propose a multi-agent learning method that combines the Bayesian method with a grouping histogram technique and the backpropagation neural network with a segment adjacent-nested technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance (Zaidan et al 2014). Banerjee and Datta (2013) propose an improved strategy for face recognition using correlation filter under varying lighting conditions and occlusion where spatial domain preprocessing is carried out by two convolution kernels.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…For example, artificial neural networks (i.e., systems that learn from data) have been used in different biometric applications involving pattern classification and identification (of a human (Dinkar andSambyal 2012, Melin et al 2012), of driver (Wu and Ye 2009), of finger-vein patterns (Wu and Liu 2011), of iris recognition (Sibai et al 2011), of human action (Youssef and Asari 2013), of gait (Zeng and Wang 2012), of the face (Connolly et al 2013;Kuo et al 2011;Choi et al 2012;Banerjee and Datta 2013;Lin and Lin 2013;Müller et al 2013), of the hand (Michael et al 2008), of the skin (Zaidan et al 2014), by keystroke (Uzun and Bicakci 2012) and by gesture, speech, handwritten text recognition and the like). Various biometric systems are being developed in such a manner (face recognition, fingerprint identification, hand geometry biometrics, retina scan, iris scan, signature, voice analysis, palm vein authentication and others).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

View full text Add to dashboard Cite

This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

show abstract

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

View full text Add to dashboard Cite

show abstract

“…Dengan jumlah data latih yang sangat besar, waktu komputasi yang dibutuhkan oleh Neural Network lebih besar daripada Bayesian Network. [8] melakukan kombinasi antara Back-Propagation Neural Network (BPNN) dengan Bayesian Network. Bayesian Network digunakan untuk mendapatkan akurasi dan kehandalan yang lebih baik meskipun dalam kondisi pencahayaan yang berbeda, sedangkan BPNN mampu menyeleksi warna piksel yang menyerupai kulit.…”

Section: Iunclassified

“…Komponen ruang warna terdiri dari komponen krominan dan luminan, sedangkan yang sering digunakan untuk warna kulit manusia adalah komponen krominan. [8] dan [9] menggunakan RGB dan YCbCr, sedangkan [10] hanya menggunakan YCbCr karena ruang warna ini memberikan hasil yang terbaik untuk warna kulit manusia. Berbeda dengan yang dilakukan oleh [11] yang menggunakan IHLS, HSI, RGB, nRGB, YCbCr, dan CIELAB yang kemudian mengeleminasi komponen luminan: L pada IHLS, I pada HSI, G pada RGB, nG pada nRGB, Y pada YCbCr, dan L pada CIELAB.…”

Section: Iunclassified

Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Afirianto¹,

Amalia²

2017

OjsInf

View full text Add to dashboard Cite

Abstract--Skin detection is a process to determine a region whether including skin or not skin. Some digital cameras produce RGB images. In many cases skin detection is transformed from RGB to other color spaces, such as HSV, YCbCr, and CIELAB. Some color spaces have two separate components, namely luminance and chrominance components, whereas the color of the human skin is more commonly in the chrominance component. In this paper, we undertake skin detection studies using chrominance components from the color spaces of HSV, YCbCr, and CIELAB, under the name HSCbCrAB. We use PCA to reduce dimensions and NN as classifier. The results of the study showed good performance in HSCbCrAB color space for skin detection.Keywords -skin detection, pca-knn, hsv, ycbcr, cielab Abstrak-Deteksi kulit merupakan suatu proses untuk menentukan suatu wilayah apakah termasuk kulit atau bukan kulit. Beberapa kamera digital menghasilkan citra RGB. Dalam berbagai kasus deteksi kulit dilakukan transformasi dari RGB ke ruang warna lainnya, seperti HSV, YCbCr, dan CIELAB. Beberapa ruang warna memiliki dua komponen yang terpisah, yaitu komponen luminan dan krominan, sedangkan warna kulit manusia lebih sering berada pada komponen krominan. Dalam paper ini, kami melakukan penelitian deteksi kulit menggunakan komponen krominan dari ruang warna HSV, YCbCr, dan CIELAB, dengan nama HSCbCrAB. Kami menggunakan PCA untuk mengurangi dimensi dank NN sebagai klasifier. Hasil dari penelitian menunjukkan performa yang bagus pada ruang warna HSCbCrAB untuk deteksi kulit.Kata Kunci -deteksi kulit, pca-knn, hsv, ycbcr, cielab I. PENDAHULUAN Banyak penelitian yang tertarik dan tertantang dengan topik deteksi kulit. Topik ini merupakan langkah awal untuk memisahkan bagian antara aktor dengan bagian latar, pelacakan pergerakan aktor, deteksi citra pornografi, pengenalan wajah, identifikasi umur, augmented reality, dan masih banyak lagi lainnya. Kakumanu et al. [1] menyatakan bahwa penelitian tentang deteksi kulit mengalami masalah besar, seperti pencahayaan, karakteristik kamera, suku/etnis, karakteristik individu, dan faktor lain, seperti makeup, gaya rambut, kacamata, keringat, dan warna latar yang menyerupai warna kulit manusia. Oleh karena itu, penelitian tentang deteksi kulit masih dikembangkan untuk menangani masalah tersebut. Suatu citra dapat dipisahkan antara piksel kulit dan bukan kulit dengan menggunakan klasifikasi, seperti teknik threshold, Bayesian Network, dan k Nearest Neighbor (k-NN). Teknik threshold memerlukan waktu komputasi yang sangat cepat karena hanya membutuhkan beberapa peraturan klasifikasi [7], akan tetapi teknik ini memiliki kelemahan, karena kurang fleksibel akibat dari peraturan yang bersifat tetap. Dengan jumlah data latih yang sangat besar, waktu komputasi yang dibutuhkan oleh Neural Network lebih besar daripada Bayesian Network.[8] melakukan kombinasi antara Back-Propagation Neural Network (BPNN) dengan Bayesian Network. Bayesian Network digunakan untuk mendapatkan akurasi dan kehandalan yang lebih baik meskipun dalam kondisi...

show abstract

“…First, the head must be segmented as much accurately as possible because our method depends highly on this step. The head can be segmented for example using GrabCut (Rother et al, 2004) or a skin detector (Zaidan et al, 2014). In this paper, GrabCut is chosen because it is a well-known and widely used method to segment images.…”

Section: Our Approachmentioning

confidence: 99%

Head Yaw Estimation using Frontal Face Detector

Mennesson

Dahmane

Danışman

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

View full text Add to dashboard Cite

Detecting accurately head orientation is an important task in systems relying on face analysis. The estimation of the horizontal rotation of the head (yaw rotation) is a key step in detecting the orientation of the face. The purpose of this paper is to use a well-known frontal face detector in order to estimate head yaw angle. Our approach consists in simulating 3D head rotations and detecting face using a frontal face detector. Indeed, head yaw angle can be estimated by determining the angle at which the 3D head must be rotated to be frontal. This approach is model-free and unsupervised (except the generic learning step of VJ algorithm). The method is experimented and compared with the state-of-the-art approaches using continuous and discrete protocols on two well-known databases : FacePix and Pointing04.

show abstract

Image skin segmentation based on multi-agent learning Bayesian and neural network

Cited by 27 publications

References 15 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Head Yaw Estimation using Frontal Face Detector

Contact Info

Product

Resources

About