Sharif Digital Repository

In this paper, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. Due to noise and illumination changes some nonfaces might be detected too, therefore we have used a skin color model in the YCbCr color space to remove some of the detected nonfaces. Finally, we have utilized SVM to detect faces more accurately. Experimental results show that the performance of the proposed method is higher than the basic AdaBoost in the sense of detecting fewer nonfaces  

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Our proposed method doesn't use the skin color information for face detection. The detection of faces is accomplished after canceling the effect of the various illumination conditions. Two completely different methods are proposed for face detection in this paper. Because of the independency of the approaches to the face's skin color, the persons with every kind of skin colors are detected even in completely dark environments. Next, the detected faces are... 

In this paper we address classification of Web content and in particular its application in the detection of pornographic Web pages. Filtering of undesirable Web content is mainly achieved based on blocking a specific Web address via searching it in a reference list of black URLs or doing a plain contextual analysis on the page by searching special keywords in the text. The main problem with current filtering methods is the requirement for instantly update of the URL list and also the high rate of over-blocking the usual pages. In this paper, we propose an intelligent approach which is based on using textual, profile, and visual features in a hierarchical structure classifier. Textual... 

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive... 

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast... 

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The detection of faces is accomplished after canceling the effect of the various illumination conditions by using a proposed filter. Because of the independency of the approach to skin color of face, the persons with every kind of skin colors are detected even in completely dark environments. Next, the detected faces are recognized. It is a well known technique to combine the feature based methods with the template based methods in face recognition. Our experiments show that we can combine some proposed aspects of the feature based... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The...