• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.1-10
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• 2011

In this paper, we proposed the skin color based hand and finger detection technology for the gesture recognition in CCTV surveillance. The aim of this paper is to present the methodology for hand detection and propose the finger detection method. The detected hand and finger can be used to implement the non-contact mouse. This technology can be used to control the home devices such as home-theater and television. Skin color is used to segment the hand region from background and contour is extracted from the segmented hand. Analysis of contour gives us the location of finger tip in the hand. After detecting the location of the fingertip, this system tracks the fingertip by using only R channel alone, and in recognition of hand motions to apply differential image, such as the removal of useless image shows a robust side. We explain about experiment which relates in fingertip tracking and finger gestures recognition, and experiment result shows the accuracy above 96%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2727-2732
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• 2014

Recently, the researches of object tracking and recognition using Microsoft's Kinect are being actively studied. In this environment human hand detection and tracking is the most basic technique for human computer interaction. This paper proposes a method of improving the accuracy of the detected hand region's boundary in the cluttered background. To do this, we combine the hand detection results using the skin color with the extracted depth image from Kinect. From the experimental results, we show that the proposed method increase the accuracy of the hand region detection than the method of detecting a hand region with a depth image only. If the proposed method is applied to the sign language or gesture recognition system it is expected to contribute much to accuracy improvement.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we present an identificable face (n face) detection method robust to color degradation and partial veiling. This method is composed of three parts: segmentation of face candidate regions, extraction of face candidate windows, and decision of veiling. In the segmentation of face candidate regions, face candidate regions are detected by finding skin color regions and facial components such as eyes, a nose and a mouth, which may have degraded colors, from an input image. In the extraction of face candidate windows, face candidate windows which have high potentials of faces are extracted in face candidate regions. In the decision of veiling, using an eigenface method, a face candidate window whose similarity with eigenfaces is maximum is determined and whether facial components of the face candidate window are veiled or not is determined in the similar way. Experimental results show that the proposed method yields better the detection rate by about $11.4\%$ in test DB containing color-degraded faces and veiled ones than a conventional method without considering color degradation and partial veiling.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.76-79
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• 2014

Face recognition is very challenging because of the unexpected changes of pose, expression, and illumination. Facial detection in the mobile environments has additional difficulty since the computational resources are very limited. This paper discusses face detection based on frequency domain matched filtering in the mobile environments. Face detection is performed by a linear or phase-only matched filter and sequential verification stages. The candidate window regions are selected by a number of peaks of the matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering tests, which aim to remove false alarms among selected candidate windows. The algorithms are built with JAVA language on the mobile device operated by the Android platform. The simulation and experimental results show that real-time face detection can be performed successfully in the mobile environments.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.339-340
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• 2007

Face detection plays an important role in HCI and face recognition. In this paper, we propose a rotation-invariant real-time face detection algorithm for color images in complex background. It consists of four processing step: (1) motion detection, (2) skin color region filler, (3) Eyemap detector for rotated face, and (4) Adaboost face classifier. This system has been tested in in-door environments, such as office and achieves over 95% detection rate.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1825-1838
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• 2016

The detection of skin pigment is crucial in the diagnosis of skin diseases and in the evaluation of medical cosmetics and hairdressing. Accuracy in the detection is a basis for the prompt cure of skin diseases. This study presents a method to recognize and measure human skin pigment using Hemoglobin-Melanin (HM) coordinate. The proposed method extracts the skin area through a Gaussian skin-color model estimated from statistical analysis and decomposes the skin area into two pigments of hemoglobin and melanin using an Independent Component Analysis (ICA) algorithm. Then, we divide the two-dimensional (2D) HM coordinate into rectangular bins and compute the location histograms of hemoglobin and melanin for all the bins. We label the skin pigment of hemoglobin, melanin, and normal skin on all bins according to the Bayesian classifier. These bin-based HM projective histograms can quantify the skin pigment and compute the standard deviation on the total quantification of skin pigments surrounding normal skin. We tested our scheme using images taken under different illumination conditions. Several cosmetic coverings were used to test the performance of the proposed method. The experimental results show that the proposed method can detect skin pigments with more accuracy and evaluate cosmetic covering effects more effectively than conventional methods.

• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.13-24
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• 2002

This paper presents an automatic approach to detect face and facial feature from face images based on the color information and deformable model. Skin color information has been widely used for face and facial feature diction since it is effective for object recognition and has less computational burden, In this paper, we propose how to compensates varying light condition and utilize the transformed YCbCr color model to detect candidates region of face and facial feature from color images, Moreover, the detected face facial feature areas are subsequently assigned to a initial condition of active contour model to extract optimal boundaries of face and facial feature by resolving initial boundary problem when the active contour is used, The experimental results show the efficiency of the proposed method, The face and facial feature information will be used for face recognition and facial feature descriptor.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.557-563
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• 2016

Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for $320{\times}240$ images, and 17.5 times for $640{\times}480$ images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.