An Efficient Skin Illumination Compensation Model for Efficient Face Detection

Kumar, C. N. Ravi; Bindu, A.

doi:10.1109/iecon.2006.348133

Cited by 27 publications

(9 citation statements)

References 10 publications

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“…An image can be represented in a number of different colour space models (i.e. RGB, HSV [1], Y C r C b [27]). These are some colour space models available in image processing.…”

Section: B Colour Spacementioning

confidence: 99%

A Fusion Approach for Efficient Human Skin Detection

Tan

Chan

Yogarajah

et al. 2012

IEEE Trans. Ind. Inf.

151

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A reliable human skin detection method that is adaptable to different human skin colours and illu- mination conditions is essential for better human skin segmentation. Even though different human skin colour detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colours across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin de- tection approach that combines a smoothed 2D histogram and Gaussian model, for automatic human skin detection in colour image(s). In our approach an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required; and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.Comment: Accepted in IEEE Transactions on Industrial Informatics, vol. 8(1), pp. 138-147, new skin detection + ground truth (Pratheepan) datase

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“…An image can be represented in a number of different colour space models (i.e. RGB, HSV [1], Y C r C b [27]). These are some colour space models available in image processing.…”

Section: B Colour Spacementioning

confidence: 99%

A Fusion Approach for Efficient Human Skin Detection

Tan

Chan

Yogarajah

et al. 2012

IEEE Trans. Ind. Inf.

151

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“…It is well established that the distribution of colors in an image is often a functional element. An image could be represented using a number of different color‐space models (e.g., RGB, Y C b C r , and HSV), and it is necessary to choose the appropriate color space for modeling facial skin color.…”

Section: Theory Foundationmentioning

confidence: 99%

An intelligent skin‐color capture method based on fuzzy C‐means with applications

Hsiao

Yen

Lee

2017

Color Research & Application

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Consumer behavior is complicated. In the cosmetic market, personal intuition and fashion trends for color selection are guidelines for consumers. A systematic method for female facial skin‐color classification and an application in the makeup market are proposed in this study. In this article, face recognition with a large number of images is first discussed. Then, an innovative method to capture color at selected points is presented and complexion‐aggregated analysis is performed. This innovative method is an extension of face‐recognition theory. Images in RGB format are converted to CIELAB format during data collection and then Fuzzy C‐means theory is used to cluster and group the data. The results are classified and grouped in Lab value and RGB index. Two programs are created. The first program, “FaceRGB,” captures color automatically from images. The second program, “ColorFCM,” clusters and groups the skin‐color information. The results can be used to assist an expert system in the selection of customized colors during makeup and new‐product development.

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“…Ten rengine bağlı yüz tespiti için yapılan çalışmalarda çeşitli renk uzayları kullanılmaktadır. Bu çalışmada, renge bağlı ayırt ediciliği arttırmak için HSI uzayının [2] yanında YCbCr uzayından [3] da faydalanılmaktadır. İlgili uzaylar kullanılarak yüz bölgesinin bulunması için yapılan eşikleme (1)'de verilmektedir.…”

Section: Ten Rengine Bağlı Yüz Tespitiunclassified

Facial feature matched one-bit transform for face recognition

Çelebi

Demir

Güllü

et al. 2008

2008 IEEE 16th Signal Processing, Communication and Applications Conference

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ÖzetçeBu bildiride, yüz tanıma için yüz özelliklerinin konumunu ve aynı zamanda yüzün yönlenmesini tespit etmek üzere yüz özelliklerine uyumlu bir-bit dönüşümü önerilmiştir. Çalışmada öncelikle ten rengine bağlı olarak yüz bölgesi bölütlenmektedir. Elde edilen yüz imgesi, özgün bir yaklaşımla en küçük kareler yöntemi ile oluşturulmuş çekirdek kullanan bir-bit dönüşümü ile ikili imgeye dönüştürülmektedir. Oluşturulan ikili imgede önceden tanımlanmış bölgeler kapsamında k-ortalama yöntemi uygulanarak göz ve ağız kümeleri belirlenmekte ve daha sonra özellik çıkartımı için kullanılacak yüz imgesi elde edilmektedir. Yüz imgesine ayrık dalgacık dönüşümü ve ayrık kosinüs dönüşümü uygulanarak özellik vektörleri bulunmakta, destek vektör makineleri kullanılarak da sınıflandırma işlemi yapılmaktadır. AbstractIn this paper, a facial feature matched one-bit transform is proposed to detect facial features and at the same time the facial orientation for face recognition. First of all the face region is segmented based on skin color. The segmented face image is then converted to a binary image with a novel facial feature matched one-bit transform that uses a kernel that is obtained by least squares. At first, eye and mouth clusters are obtained using a k-means based approach in predefined regions of the constructed binary image, and the image of the face is obtained. Then, feature vectors are extracted using discrete wavelet transform and discrete cosine transform and classification is achieved using support vector machines.

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An Efficient Skin Illumination Compensation Model for Efficient Face Detection

Cited by 27 publications

References 10 publications

A Fusion Approach for Efficient Human Skin Detection

A Fusion Approach for Efficient Human Skin Detection

An intelligent skin‐color capture method based on fuzzy C‐means with applications

Facial feature matched one-bit transform for face recognition

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