<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in image based on haar-like features to represent the invariant characteristics of a face. The choice relevant and more representative is divine proportions This technique, widely used world beauty, especially aesthetic medicine, allows face be divided into set specific...

We present an application of Viola-Jones face detection and exemplar-based inpainting to automatically select and remove unwanted background person(s) in photographs. We experiment with using various source and target region masks as well as patch and neighbourhood selections to produce realistic inpaintings. The final pipeline is able to generate results comparable to those achieved with manua...

A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a “chain” structure is introduced to integrate historical knowledge into successive boosting learning. Moreover, a linear optimization scheme is proposed to address the problems of redundancy in boosting learning and threshold adjusting in cascade coupling. By this means, the...

An effective and real-time eyes detection system based on the symmetric and geometric relationships of human-eyes region in gray-level images is presented. In order to reduce the search effort and eliminate the noise impact, we first perform the edge processing with Sobel filter. We then make use of the characteristics of symmetric relationship of human-eyes region to find out the possible regi...

A novel face detector using the non-linear Fuzzy Integral operator is presented in this paper. The main advantage of this method is that it has a much lower false detection rate with the same optimal set of features as the state-of-the art Adaboost face detector. Furthermore, this novel face detector seems to have a better generalization capability than the Adaboost method. Preliminary results ...

Current systems for object detection often use support vector machines (SVMs) as the basic classification algorithm. A rather common case is to compute a small set of linear features and then train the classifier on these features. We present a fast method to train and evaluate SVMs with many linear features and show results for face detection using a set of 210,400 features. The resulting clas...

Object detectors based on the sliding window technique are usually trained in two successive steps: first, an initial classifier is trained on a population of positive samples (i.e. images of the object to detect) and negative samples randomly extracted from scenes which do not contain the object to detect. Then, the scenes are scanned with that initial classifier to enrich the initial set with...

This paper describes a novel method LogAbout to compensate variety of illumination in face detection. The highlight and shadow parts of the image are rendered further away from the transformation we proposed, thus increasing the image contrast. Some traditional methods based on spatial-domain are introduced in this paper. Experiments and Comparisons with each scheme we introduced are presented....

Omnidirectional vision sensors are mainly used for geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the omnidirectional images. The face detection algorithm trained on perspective images is then applied on the unwrapped im...