Face detection method using template feature and skin color feature in rgb color space

In this paper, propose a method of face detection based on adaptive skin color and structure model for multi-pose color images in a complex background. First, a face skin color model is built, and an adaptive Gamma correction is used in the skin color segmentation, which improve the skin color model to adapt to different illumination and multi-poses of face. Then a triangular structure model is constructed specific to the face features. And finally use the structure model to locate the face precisely on the basis of face images of skin color. The experimental results show the approach raised on this paper can adapt to the face detection under the conditions of different illumination environment, various poses and complex background, and the algorithm is quick and effective.

A method for the detection and tracking of human face in color images is described in this paper. A skin color model in r,g,b chrominance space is used for segmenting skin color regions from non-skin color region. The best-fit rectangle of the skin color region is labeled as a candidate face. But the face skin color is sensitive to the change of the environment illumination. An algorithm is proposed to update the skin color model's parameters in time so that the model is adapted to different lighting conditions. The confidence measure is presented to evaluate the reliability of skin color model. Experiments demonstrate that the self-adaptive color model is more effective than the fixed model. The color adaptation makes that the color model can be better fit to the more complex application environment.

This paper proposed a fast facedetection method based on the skin color feature and local binary gradient feature. First, according to the clustering of human skin color in the YCbCr color space, the skin color area is detected in the image. Then, it is coarsely fast judged whether if the face is in the skin color area. Finally, the local binary gradient feature is used to judge face accurately, and the weights of the local binary gradient feature is found by using AdaBoost train algorithm. For improved the efficiency of algorithm, the algorithm is accelerated by using the method of the integral image, the cascade classifier and the search order from big to small. The algorithm was tested by a set with 450 color images which the size is 896 ×592. It can find that the average detection time of new algorithm has reduced about 17.1% compare with the face detection algorithm of Paul Viola. The detection accuracy is similar with algorithm of Paul Viola.

This paper proposes a novel technique for detecting faces in color images using AdaBoost algorithm combined with skin color segmentation. First,skin color model in the YCbCr chrominance space is built to segment the non-skin-color pixels from the image. Then, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so we can extract candidate human face regions. Finally, these face candidates are scanned by cascade classifier based on AdaBoost for more accurate face detection. This system detects human face in different scales, various poses, different expressions, lighting conditions, and orientation. Experimental results show the proposed system obtains competitive results and improves detection performance substantially.

Man machine interface by video analysis becomes popular recently. The most frequently adopted body parts for computer interaction are face and hands. Therefore, it is a very important topic to accurately extract face and hand regions from a sequence of images in real time. In this paper, we propose an adaptive skin color model which is based on face detection. Skin colors were sampled from extracted face region where non-skin color pixels like eyebrow or eyeglasses could be excluded. Gaussian distributions of normalized RGB were then used to define the skin color model for the detected person. To demonstrate the robustness of proposed model, experiments under diversified lighting and background were tested. Traditional methods based on RGB, Normalized RGB, and YCbCr were all implemented for comparison. From experimental results, skin color pixels could be detected for each person. The accuracy rate is 95.73% on average and is superior to previously mentioned methods.

The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.

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.

Automatic face detection in colored images is closely related to face recognition systems, as a preliminary critical required step, where it is necessary to search for the precise face location. We propose a reliable approach for skin color segmentation to detect human face in colored images under unconstrained scene conditions that overcoming the sensitivity to the variation in face size, pose, location, lighting conditions, and complex background. Our approach is based on building multi skin color clustering models using HSV color space, multi-level segmentation, and rule-based classifier. We proposed to use four skin color clustering models instead of single skin clustering model, namely: standard-skin model, shadow-skin model, light-skin model, high-red-skin model. We made an independent skin color clustering models by converting 3-D color space to 2-D without losing color information in order to find the classification boundaries for each skin color pattern class in 2-D. Once we find the classification boundaries, we process the input image with the first-level skin-color segmentation to produce four layers; each layer reflecting its skin-color clustering model. Then an iterative rule-based region grow is performed to create one solid region of interest which is presumed to be a face candidate region that will be passed to the second-level segmentation. In this approach we combine pixel-based segmentation and region-based segmentation using the four skin layers. We also propose skin-color correction (skin lighting) at shadow-skin layer to improve detection rate. In the second-level segmentation we use gray scale to segment the face candidate region into the most significant features using thresholding. Next step is to compute the X-Y-reliefs to locate the accurate position of facial features in each face candidate region and match it with our geometrical knowledge in order to classify the face candidate region to a face or non-face region. We present experimental results of our implementation and demonstrate the feasibility of our approach to be general purpose skin color segmentation for face detection problem.

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Face detection is a key step in computer vision applications, such as face recognition and video surveillance. There is a difficult question in automatically segmenting color images into skin color and background regions when using the method of skin color segmentation. The border of skin color region has some background noise patches, especially using edge detection to extract facial organ characteristics, which will lead to some redundant gradient features near the border. In this paper, we introduce a new method based on the histogram to smooth face contour and reduce background noise patches. The performance of our face detector based on the smoothing contour algorithm has been tested on reference test sets, and clearly show some improvements in face detection which is based on knowledge-based methodology.

To realize the eye pupil center location in color face image, a new algorithm based on skin color segmentation and radial symmetry transform is proposed. The algorithm makes full use of the pupil and the surrounding region’s gradient variation characteristics and radial symmetry characteristic, which can effectively solve the interference factors such as uneven spot, frame. Step: First, use the skin color model to detect the face region, and then remove the small block of the background with similar skin color by morphological opening operation and area comparison filtering method. Then, use the horizontal integral projection to determine the candidate eye regions. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Experimental results show that the proposed algorithm has good localization effect, and has strong robustness to various disturbances. Introduction In recent years, line of sight tracking technology is an important and active research area of computer vision. It is widely used in human-computer interaction, auxiliary driving vehicles, virtual reality and military fields [1]. An important process in the line of sight tracking system is the extraction and detection of line of sight characteristic parameters. Eyes as the most important visual organ contains a wealth of sight feature parameters such as the pupil center. Therefore, a fast, accurate and effective eye location algorithm is very important. Proposed Method Fig.1 shows the pupil center location algorithm flow. First, detect face region by the skin color model in the YCbCr color space. Then, using gray level features of eye region, the candidate eye region is detected by the horizontal integral gray projection algorithm [2, 3, 4]. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Face Detection Based on Skin Color Segmentation. Skin color is one of the most striking features of color face images, which is relatively concentrative and stable, and not affected by the brightness of the image. Skin color has a strong clustering feature. Use the ellipse skin color model proposed by Hsu[5] based on YCbCr color space, Where the Y represents the brightness information, and Cb and Cr represent the blue chrominance component the red chrominance component respectively. Ellipse skin color model is shown in Eq.1 and Eq.2, Where Cband Cr represent the original blue component and red component in the YCbCr color space respectively, and x and y represent chrominance components after transformation. Bring X, Y into the left of 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015) © 2015. The authors Published by Atlantis Press 1040

Skin color is an important feature for face detection in color images. By building and applying a statistical skin color model, possible face regions in color images can be obtained. However, due to the inclusion of different races of people's skin color points, this general statistical model is not accurate enough to segment each specific image as we expected. Besides, most 2D skin color models, e.g. CbCr, which is a 2D projection of 3D color distribution, cannot adapt itself to lighting variations. So, we present a 3D CrCbCg model to describe the skin color distribution more precisely. Meanwhile, considering skin points in a specific image have a relatively stable distribution, we present a fuzzy cluster based skin model to remove background points which are wrongly retained by the general model. Experimental results show that our algorithm can effectively improve segmentation results.

This paper proposes a new structure for applying to real-time evolutionary face tracking of the streaming images. In the described method, first we use the features such as skin color models and facial proportions to extract the face region and complete the pre-processing, and then track the moving face location with particle swarm optimization algorithm. The experimental results show that the face detection method which we have developed has a higher detection rate and complex backgrounds tolerance than the traditional Viola-Jones detector. Compared to the method of searching the face region in the whole image with the streaming image sequences one by one, the execution effect of searching the face region in the key region images by using the method of evolutionary face tracking system based on particle swarm optimization algorithm has a fast execution speed and better accuracy. Furthermore, in order to achieve the goal of real-time processing, we also complete the software design and verify with the personal computer as well as Nios II embedded processor-based FPGA platform, and analyze the efficiencies of various modules to find the functional module of efficiency bottleneck for the further implementation of a hardware architecture, to improve the overall efficiency of the system through a hardware/software co-design. Experimental results demonstrate that the proposed structure is efficient and robust in face tracking under dynamic environments with real-time performance.

Face detection is no longer necessarily correlated with face recognition. Instead it has been established as an important tool in the framework of many multimedia applications like indexing, scene classification and news summarization. Many face detection algorithms based on skin color characteristics have appeared in the literature. Most of them face generalization problems due to the skin color model they use. Moreover, their verification stage exclusively depends on simple shape features limiting the reliability of detection. In this work we present a Gaussian model of the skin color distribution whose parameters are re-estimated based on the current image I frame. In this way the generalization problem is limited. Furthermore the verification stage, applied in the detected skin segments, is based on a template matching variation providing a robust detection.

Human face detection has become a major field of interest in current research because there is no deterministic algorithm to find face(s) in a given image. Further the algorithms that exist are very much specific to the kind of images they would take as input and detect faces. The problem is to detect faces in the given, colored group photograph. In this paper, an improved segmentation algorithm for face detection in color images with multiple faces and skin tone regions is proposed. Algorithm ingeniously uses a novel skin color model, RGB-HS-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin color distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin color and background environment.