Skin Color Segmentation Research Articles

On Pose Estimation for Human-Robot Symbiosis

This paper presents a vision based pose estimation system using knowledge based approach for human-robot symbiosis. The system is based on visual information of the face by connected component analysis of the skin color segmentation of images in HSV color model and is commenced with the face recognition and pose classification scheme using subspace PCA based pattern-matching strategies. With the knowledge of the known user's profile, face poses are then classified by multilayer perceptron. Based on the frame-based knowledge representation approach, face poses are being interpreted using the Software Platform for Agent and Knowledge (SPAK) management. On face pose recognition, robot is then instructed to perform some specific tasks by issuing pose commands. Experimental results demonstrate that the subspace method is better than that of the standard PCA method for face pose classification. The system has been demonstrated with the implementation of the algorithm to interact with an entertainment robot named, AIBO for human-robot symbiotic relationship.

Fuzzy System Learned Through Fuzzy Clustering and Support Vector Machine for Human Skin Color Segmentation

This paper proposes a Fuzzy System learned through Fuzzy Clustering and Support Vector Machine (FS-FCSVM). The FS-FCSVM is a fuzzy system constructed by fuzzy if-then rules with fuzzy singletons in the consequence. The structure of FS-FCSVM is constructed by fuzzy clustering on the input data, which helps to reduce the number of rules. Parameters in FS-FCSVM are learned through a support vector machine (SVM) for the purpose of achieving higher generalization ability. In contrast to nonlinear kernel-based SVM or some other fuzzy systems with a support vector learning mechanism, both the number of parameters/rules in FS-FCSVM and the computation time are much smaller. FS-FCSVM is applied to skin color segmentation. For color information representation, different types of features based on scaled hue and saturation color space are used. Comparisons with a fuzzy neural network, the Gaussian kernel SVM, and mixture of Gaussian classifiers are performed to show the advantage of FS-FCSVM.

Hybrid-boost learning for multi-pose face detection and facial expression recognition

This paper proposes a hybrid-boost learning algorithm for multi-pose face detection and facial expression recognition. To speed-up the detection process, the system searches the entire frame for the potential face regions by using skin color detection and segmentation. Then it scans the skin color segments of the image and applies the weak classifiers along with the strong classifier for face detection and expression classification. This system detects human face in different scales, various poses, different expressions, partial-occlusion, and defocus. Our major contribution is proposing the weak hybrid classifiers selection based on the Harr-like (local) features and Gabor (global) features. The multi-pose face detection algorithm can also be modified for facial expression recognition. The experimental results show that our face detection system and facial expression recognition system have better performance than the other classifiers.

Skin color and feature-based segmentation for face localization

This paper presents a combination of a color- and a morphological-based method for face localization in color images. The skin-color segmentation technique is first applied using a color space, YCgCr, specifically proposed for face detection, in order to obtain the skin regions inside the image. Then, a combination of morphological operators and algorithms is used for completing the segmentation masks. In a second phase, the location of the facial features, based on the elliptical shape of the face, is performed for finding the face candidates. Therefore, the definition of a simple face model based on the best-fit ellipse determined by the position of the eyes and mouth is proposed, and its application to the segmentation mask yields the localization of the face. Finally, the segmentation mask based on this ellipse is used for detecting the face in the image under test.

A visual approach for driver inattention detection

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.

Classifiers for driver activity monitoring

The goal of this work is the detection and classification of driver activities in an automobile using computer vision. To this end, this paper presents a novel two-step classification algorithm, namely, an unsupervised clustering algorithm for grouping the actions of a driver during a certain period of time, followed by a supervised activity classification algorithm. The main contribution of this work is the combination of the two methods to provide a computationally fast solution for deployment in real-world scenarios that is robust to illumination and segmentation issues under most conditions experienced in the automobile environment. The unsupervised clustering groups the actions of the driver based on the relative motion detected using a skin-color segmentation algorithm, while the activity classifier is a binary Bayesian eigenimage classifier. Activities are grouped as safe or unsafe and the results of the classification are shown on several subjects obtained from two distinct driving video sequences.

Detection Method of Skin Region by Near-IR Spectrum Multi-Band

Many active safety technologies for the driver support system are developing. Most of the traffic accidents are caused by driver's inattentive or drowsy. We are developing a driver support system that protects from traffic accidents by these causes. Our purpose is to detect the driver's face region by using a camera. A lot of face detection methods are proposed, but there is not a technique addressing every environment inside the car. For example, skin color segmentation can not detect the skin region in the night, because it has to light up the driver by bright light. In this paper, we propose a skin detection method by the unique reflection characteristics of the materials. Our method is very simple algorithm. We developed a skin detection system, and confirmed effectiveness by the evaluation experiment in indoor environment, and showed the effectiveness by a driving experiment in the night.

ロバスト人物検出によるハイブリッドカメラサーベイランスシステム

We propose a novel surveillance system that uses hybrid camera network. The system contains a panorama camera and PTZ cameras to take the wide range images and face images at high enough resolution for identification tasks. The robust human detection methods include robust background subtraction method (RRC), skin color segmentation and face tracking method. First, the system detects persons from a panorama image, and then the detailed face images are obtained with the PTZ cameras. The PTZ cameras can track the faces using the four directional features and the relaxation matching. In addition, the system has automatic camera position calibration feature. Thus, the user can use the system without any troublesome settings.

DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL

In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented regions is then searched for the facial features using the LS face model to detect the face present in it. The LS face model is a geometric approach in which the spatial relationships among the facial features are determined for the purpose of face detection. Hence, the proposed approach based on the combination of skin color segmentation and LS face model is able to detect single as well as multiple faces present in a given image. The experimental results and comparative analysis demonstrate the effectiveness of this approach.

Efficient color face detection algorithm under different lighting conditions

We present an efficient and reliable algorithm to detect human faces in an image under different lighting conditions. In our algorithm, skin-colored pixels are identified using a region-based approach, which can provide more reliable skin color segmentation under various lighting conditions. In addition, to compensate for ex- treme lighting conditions, a color compensation scheme is pro- posed, and the distributions of the skin-color components under various illuminations are modeled by means of the maximum- likelihood method. With the skin-color regions detected, a ratio method is proposed to determine the possible positions of the eyes in the image. Two eye candidates form a possible face region, which is then verified as a face or not by means of a two-stage procedure with an eigenmask. Finally, the face boundary region of a face can- didate is further verified by a probabilistic approach to reduce the chance of false alarms. Experimental results based on the HHI MPEG-7 face database, the AR face database, and the CMU pose, illumination, and expression (PIE) database show that this face de- tection algorithm is efficient and reliable under different lighting con- ditions and facial expressions. © 2006 SPIE and

Segmentation of the Face and Hands in Sign Language Video Sequences Using Color and Motion Cues

We present a hand and face segmentation methodology using color and motion cues for the content-based representation of sign language video sequences. The methodology consists of three stages: skin-color segmentation; change detection; face and hand segmentation mask generation. In skin-color segmentation, a universal color-model is derived and image pixels are classified as skin or nonskin based on their Mahalanobis distance. We derive a segmentation threshold for the classifier. The aim of change detection is to localize moving objects in a video sequences. The change detection technique is based on the F test and block-based motion estimation. Finally, the results from skin-color segmentation and change detection are analyzed to segment the face and hands. The performance of the algorithm is illustrated by simulations carried out on standard test sequences.

Skin color-based video segmentation under time-varying illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling, and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24 percent is observed in 17 out of 21 test sequences. In all but one case, the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

Gesture-Tracking in Real Time with Dynamic Regional Range Computation

This paper presents a new approach to the range data utilization in a gesture-tracking system. The use of three-dimensional data is essential for human motion analysis; however, the speed of complete range estimation prohibits from including it in most real-time systems. This work describes a gesture-tracking system using real-time local range on-demand. The system represents a gesture-controlled interface for interactive visual exploration of large data sets. The paper describes a method performing range processing only when necessary and where necessary. Range data is processed only for non-static regions of interest. This is accomplished by a set of filters on the color, motion, and range data. The speed-up achieved is between 1.70 and 2.15. The algorithm also includes a robust skin-color segmentation insensitive to illumination changes. Selective range processing results in dynamic regional range images that contain only information needed by the system.

An automatic algorithm for semantic object generation and temporal tracking

Automatic semantic video object extraction is an important step for providing content-based video coding, indexing and retrieval. However, it is very difficult to design a generic semantic video object extraction technique, which can provide variant semantic video objects by using the same function. Since the presence and absence of persons in an image sequence provide important clues about video content, automatic face detection and human being generation are very attractive for content-based video database applications. For this reason, we propose a novel face detection and semantic human object generation algorithm. The homogeneous image regions with accurate boundaries are first obtained by integrating the results of color edge detection and region growing procedures. The human faces are detected from these homogeneous image regions by using skin color segmentation and facial filters. These detected faces are then used as object seed for semantic human object generation. The correspondences of the detected faces and semantic human objects along time axis are further exploited by a contour-based temporal tracking procedure.

ASL characters recognition using person specific subspace method

This paper presents a person-specific subspace method for ASL characters recognition. To implement this person specific subspace method for ASL characters recognition the system first recognizes the faces and then uses skin-color segmentation method for hand poses segmentation. From the segmented hand poses the system recognizes ASL characters using both PCA and person-specific subspace method separately. Experiment result shows that the classification accuracy of person-specific subspace method is better than general PCA method. The experiment result also shows that person-specific subspace method is faster than general PCA method. Key Words: ASL character, Face recognition, PCA, Person-specific subspace method DOI: 10.3329/diujst.v4i2.4366 Daffodil International University Journal of Science and Technology Vol.4(2) 2009 pp.39-52