Detection and Segmentation of Human Face

2008 Second International Conference on Secure System Integration and Reliability Improvement, 2008

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

IOSR Journal of Computer Engineering, 2013

Human face recognition systems have gained a considerable attention during last decade due to its vast applications in the field of computer and advantages over previous biometric methods. There are many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. This paper introduces a new approach to face detection systems using the skin color of a subject. This system can detect a face regardless of the background of the picture, which is an important phase for face identification. The images used in this system are color images which give additional information about the images than the gray images provide. In face detection, the two respective classes are the "face area" and the "non-face area". This new approach to face detection is based on color tone values specially defined for skin area detection within the image frame. This system first resizes the image, and then separates it into its component R, G, and B bands. These bands are transformed into another color space which is YCbCr space and then into YC'bC'r space (the skin color tone). The morphological process is implemented on the presented image to make it more accurate. At last, the projection face area is taken by this system to determine the face area. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 92.69%.

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)International Association of Scientific Innovation and Research (IASIR), 2013

Human face detection plays important roles in many applications such as video surveillance and face image database management. In our Paper, we have studied worked on face detection By YCbCrHs Techniques and developed algorithms for them. In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin color as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully. Our aim, which we believe we have reached, was to develop a method of face recognition that is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and. techniques. The examples provided in this Paper are real-time and taken from our own surroundings. While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. In This Paper presents a novel skin colour model, RGB-YCbCrHs for the detection of human faces.

2006

In this paper, we develop two face detection methods in color images. These methods rely on a two step process. First, we detect human skin regions using Bayes rule in color images. To avoid the effect of brightness included in the RGB color space, we propose to model the skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. To localize the faces within the detected skin regions, two different face detection techniques are then performed. The first algorithm is based on a Template Matching method using the Support Vector Machines "SVM". The second one consists on a multi-layer Neural Network classification. In the following, experiments are carried out and satisfactory results are obtained which indicate the robustness of the first process to detect faces under different environment conditions.

iaeme

The growth of population and technology demand security surveillance behind abnormal human behaviours. Now a day’s colour human face detection is complicated task in the field of biometrics research. In this paper we used new approach for colour human face detection based on C 4.5 data classifier and YCbCr colour model for the detection of face skin colour , also used Canny edge detection technique for the finding a boundary edge of face. This method simulated MATLAB software and getting good result.

2011

—This article is devoted to introducing a new algorithm for face detection. This method is based on using an algorithm for skin color, and template matching. First, we determined the skin points by considering the standard skin colors. This considering rejects many parts from either the pictures or video images. Then, using the template matching the different parts is combined, and many errors due to the mismatch of the first algorithm, and some noises are reduced vitally. Our simulation results indicate that both positive and negative errors have insignificant.

Journal of Reliable Intelligent Environments, 2016

Human face detection has become an area of interest in various biometric applications such as crowd surveillance, human-computer interaction, and many security related areas. It is a major field of current research because there is no deterministic algorithm to find the face(s) in a given image. Face detection is challenging due to varying illumination conditions, pose variations, the complexity of noises and image backgrounds. In this paper, a localized approach for face detection based on skin color segmentation and facial features is proposed. Algorithm proficiently analyzes various skin color models such as RG B, Y CbCr, H SV and their combinations for the skin color detection because skin segmentation decreases the computational complexity. The segmented face regions are further classified using a set of facial features such as eye-mouth hole detection, bounding box, and eccentricity ratio. This method is tested on four databases: Bao database contains 157 images, Muct database contains 751 images, F E I database contains 2800 images and GT AV database contains 1188 images. The algorithm achieves an average accuracy of 97.85 %. Comparison with Viola Jones, face detection using skin color model, and fast face detection based on skin segmentation and facial features methods has also been done. Further, we apply this method to cartoon characters. Our method works with images with some conditions. First, input images are not monochrome images but color images. Second, the characters skin color has to be near real people skin color. Skin color region is extracted from the image and after this apply the facial fea-B Shalini Yadav

International Journal of Computer and Electrical Engineering, 2010

robust face detection in complex background is a challenging task. The complexity in such detection systems stems from the variances in image background, view, illumination, articulation, and facial expression. This article is allocated to introducing a new algorithm for face detection. Fuzzy skin color detection and template matching composed our method instrument. At this paper non-skin parts in the pictures omitted firstly and then result passed to template matching section. Easy implementation and high accuracy detection be feature of our work in compare with other works and indicate the superiority of our work.

Digital Image Computing: …, 2003

Mahalanobis distance has already proved its strength in human skin detection using a set of skin values. We present this work that uses automatic skin detection after an initial camera calibration. The calibration is done by human sampling from test individuals. A scaling is performed on the work data, before applying the Mahalanobis distance that ensures better results than previous works. We use the TSL color space also used successfully by others authors, where undesired effects are reduced and the skin distribution fits better in a Gaussian model than in others color spaces. Also, using an initial filter, normally large areas of easily distinct non skin pixels, are eliminated from further processing. Analyzing and grouping the resulting elements from the discriminator, improves the ratio of correct detection and reduce the small non skin areas present in a common complex image background, including Asiatic, Caucasian, African and interracial descent persons. Also this method is not restricted to orientation, size or grouping candidates. The present work is a first step in a approach for human face detection in color images, but not limited in any way to this goal.