Real-time hands, face and facial features detection and tracking: Application to cognitive rehabilitation tests monitoring

González-Ortega, D.; Pernas, Francisco Javier Díaz; Martínez‐Zarzuela, Mario; Antón-Rodríguez, M.; Díez-Higuera, J. F.; Boto-Giralda, D.

doi:10.1016/j.jnca.2010.02.001

Cited by 19 publications

(10 citation statements)

References 51 publications

(59 reference statements)

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“…Oleh karena itu, penelitian tentang deteksi kulit masih dikembangkan untuk menangani masalah tersebut. Dalam penelitian deteksi kulit, [2] dan [3] menggunakan ektrasi kulit untuk mendeteksi wajah, [4] dan [5] mendeteksi citra pornografi, sedangkan [6] mendeteksi wajah, mata, hidung, dan tangan kemudian melacak bagian-bagian tubuh tersebut.…”

Section: Iunclassified

“…Langkah yang dilakukan pada tahap ini adalah: 1. ruang warna dari citra data latih dikonversi dari RGB menjadi HSV, YCbCr, dan CIELAB, 2. setiap komponen krominan dari ruang warna (HSCbCrAB) diambil kemudian disimpan ke dalam matriks, 3. setiap piksel dari komponen yang telah diambil selanjutnya diberi label sebagai kelas kulit atau bukan kulit, 4. mengurangi jumlah data latih berdasarkan label masing-masing citra sebesar n-klaster dengan menggunakan k-Means, 5. menyimpan pusat setiap klaster sebagai piksel baru, Gambar 1. Metode yang diusulkan untuk deteksi kulit 6. memilih fitur ruang warna yang memiliki variasi tertinggi menggunakan PCA (memiliki nilai eigen tertinggi), 7. menyimpan nilai mean (rerata) dari data latih untuk setiap fitur ruang warna dan vektor eigen yang telah terpilih dari PCA, dan 8. melakukan matriks transformasi data latih terhadap vektor eigen.…”

Section: Metode Penelitianunclassified

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Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Afirianto¹,

Amalia²

2017

OjsInf

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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...

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Section: Iunclassified

Section: Metode Penelitianunclassified

Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Afirianto¹,

Amalia²

2017

OjsInf

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“…The inputs to the neural network are the chromaticity components (T and S) of the TSL color space of all the pixels inside the skin region of an image. The TSL color space was selected as it achieved the best skin color filtering performance in an experimental study including the normalized rgb, YIQ, HIS, CIELUV, and TSL space (González-Ortega et al, 2010). The application has been developed using the Intel Open Source Computer Vision Library (Open Computer Vision Library, 2010) and the FLTK GUI Library (FLTK Library, 2010).…”

Section: Skin Color Filter Applicationmentioning

confidence: 99%

Self Organizing Neural Network Application for Skin Color Segmentation

González-Ortega¹,

Pernas²,

Antón-Rodríguez³

et al. 2010

Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation

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In this paper, we present a Fuzzy ART (Adaptive Resonance Theory) neural network application for skin color segmentation using the chromaticity components of the TSL color space. The Fuzzy ART networks deal with the stability-plasticity dilemma and they can be applied to color image segmentation, particularly to skin color segmentation. The developed application has three modes: parameter setting, skin color filter creation, and skin color filter performance. Many parameters can be tuned to create proper skin color filters from manually selected skin regions in an image. A skin color filter is a LUT (Look-Up Table) that gives each color in the RGB color space, one of two different outputs, skin or non-skin color. The performance of different skin color filters can be compared with the application. A skin color filter can be used to make robust real-time skin color segmentation in video sequences captured by a webcam.

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“…Visual human motion tracking systems [3] conduct 3D localization of the patient's body and limbs by combining data of several cameras recording the patient from different perspectives. Marker-free systems [4] track the boundaries of human body while marker-based systems, such as CODA [5] or Qualisys [6], track either light reflective markers (passive markers) or light-emitting diodes (active markers) attached to the patient. Such systems have shown promising performance in rehabilitation supervision due to their accurate localization (error around 1mm [2]).…”

Section: Introductionmentioning

confidence: 99%

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Hadjidj¹,

Souil²,

Bouabdallah³

et al. 2013

Journal of Network and Computer Applications

139

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Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges.

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Real-time hands, face and facial features detection and tracking: Application to cognitive rehabilitation tests monitoring

Cited by 19 publications

References 51 publications

Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN

Self Organizing Neural Network Application for Skin Color Segmentation

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

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