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J. Visual Communication and Image Representation. 01/2012; 23:303-312.
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ABSTRACT: In this paper, we propose a method of eye detection based on skin color analysis under varying illumination. The proposed method consists of several phases, including color conversion, skin color segmentation and face mask calculation, facial feature extraction and eye candidate determination, and detection of human eyes. To eliminate the effect of lighting change on the performance of eye detection, color conversion is first performed. Face mask calculation based on skin color segmentation is then carried out to reduce the possible searching region of human eyes. Eye candidates detected using facial features are used to define the possible human eyes. Human eyes are thus detected by the geometric features of gravity and spatial centers for these eye candidates. Results show that the proposed method works well for faces with different poses and multiple faces under varying illumination. The eye detection time of 21.8 ms is achieved for an image of size 213×320 pixels, indicating computational efficiency of the proposed system.
Genetic and Evolutionary Computing (ICGEC), 2011 Fifth International Conference on; 10/2011
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11th International Conference on Hybrid Intelligent Systems, HIS 2011, Melacca, Malaysia, December 5-8, 2011; 01/2011
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Expert Syst. Appl. 01/2011; 38:6031-6042.
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ABSTRACT: This paper proposes a three-stage scheme to obtain real-time and reliable face detection in intersecting monitoring. The proposed three-stage scheme is mainly based on skin color and facial features. In the first stage scheme, skin-color model is used to obtain skin regions. The second stage scheme uses face template measure to obtain face candidates. Finally, facial features are measured to detect faces from face candidates. Experimental results show that the proposed method has good performance in face detection.
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on; 01/2010
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Computational Collective Intelligence. Technologies and Applications - Second International Conference, ICCCI 2010, Kaohsiung, Taiwan, November 10-12, 2010. Proceedings, Part III; 01/2010
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Computational Collective Intelligence. Technologies and Applications - Second International Conference, ICCCI 2010, Kaohsiung, Taiwan, November 10-12, 2010. Proceedings, Part III; 01/2010
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Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2009), Kyoto, Japan, 12-14 September, 2009, Proceedings; 01/2009
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9th International Conference on Hybrid Intelligent Systems (HIS 2009), August 12-14, 2009, Shenyang, China; 01/2009
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ABSTRACT: Face detection has a variety of applications, such as face recognition, face expression analysis, and video conferencing. However, most existing methods for face detection are sensitive to lighting variation. In this paper, a novel scheme invariant with illumination based on adaptive switching of skin color models (ASSM) with lighting compensation for face detection is proposed. The skin-tone pixels detected are connected by the proposed fast 8-connected component labeling method into a more compact skin cluster. An optimal skin color model is thus adaptively selected using a well-defined quality measure. Possible face candidates are further validated by a cascaded AdaBoost detector. Experimental results indicate that robust face detection can be achieved for various lighting conditions, such as dim light, side light, and back light. A detection time of 60 ms for each frame is achieved with the aid of the ASSM method. A detection rate of 94.4% was obtained for a test video sequence.
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ABSTRACT: A three-stage scheme for real-time reliable face detection is presented. The proposed three-stage scheme is a feature-based method that is mainly based on skin color and facial features. Skin regions are obtained using a YCbCr skin-color model in the first stage. In the second stage, a face template measure is used to obtain face candidates and then a suitable face box is used to effectively remove non-face regions from the face can-didates. Finally, facial features are measured to detect faces from face candidates in the third stage. Experimental results show that the proposed method has good performance in the face detection of faces in various poses, faces in skin color-like backgrounds, faces under varying illumination, and faces of various races.
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ABSTRACT: Due to limited resource contentions and deadline constraints, messages on the Controller Area Network (CAN) are competing for service from the common resources. Actually, because of the fluctuation of network traffic or an inefficient use of resources, the existing static or dynamic priority policies may not guarantee flexibility for different kinds of messages in real-time scheduling. Consequently, the message transmission which cannot comply with the timing requirements or deadlines may deteriorate system performance significantly. In this paper, we have proposed a controller-plant model, where the plant is analogous to a message queue pool (MQP) and the message scheduling controller (MSC) is responsible to dispatch resources for queued messages. The MSC, which is realized by the radial basis function (RBF) network, is designed with machine learning algorithm to compensate the variations in plant dynamics. The MSC with the novel hybrid learning schemes can ensure a low and stable message waiting time variance and lower transmission failures. A significant emphasis of the MSC is the variable structure of the RBF model to accommodate to complex scheduling situations. Simulation experiments have shown that several variants of the MSC significantly improve overall system performance over the static scheduling strategies and the dynamic Earliest Deadline First (EDF) algorithms under a wide range of workload characteristics and execution environments.
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ABSTRACT: In this paper, we propose a method of face detection based on feature analysis and edge detection against skin color-like backgrounds. The proposed method consists of three phases including image preprocessing, skin color segmentation, and determination of face candidates. First, the objects in foreground are separated by image preprocessing. Second, the non-skin color objects are rejected by the method of skin color segmentation. Finally, both the elimination of skin color-like blobs in backgrounds and face detection are performed by the method of determination of face candidates. Experimental results show that the proposed method can effectively detect most of faces in skin color-like backgrounds. In addition, detection of face images with pose and expression variations or against dark backgrounds are also carried out.
Genetic and Evolutionary Computing, International Conference on.
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ABSTRACT: In this paper, an adaptive skin color model switching based on AdaBoost method for face tracking is proposed. Possible skin clusters under illumination varying scenes are detected by an optimal skin color model, which is adaptively selected by a well-defined quality measure. The possible facial candidates are further validated by AdaBoost to determine whether human faces exist in video sequences or not. The tracking sequences reveal that good and robust results are obtained from dim- to profile- to back-light scenarios. The performance of the proposed method can achieve an average tracking time of about 145.4 ms/frame and a detection rate of 94.4%.
Innovative Computing ,Information and Control, International Conference on.
