Elastic graph matching has been proposed as a practical implementation of dynamic link matching, which is a neural network with dynamically evolving links between a reference model and an input image. Each node of the graph contains features that characterize the neighborhood of its location in the image. The elastic graph matching usually consists of two consecutive steps, namely a matching with a rigid grid, followed by a deformation of the grid, which is actually the elastic part. The deformation step is introduced in order to allow for some deformation, rotation, and scaling of the object to be matched. This method is applied here to the authentication of human faces where candidates claim an identity that is to be checked. The matching error as originally suggested is not powerful enough to provide satisfying results in this case. We introduce an automatic weighting of the nodes according to their significance. We also explore the significance of the elastic deformation for an application of face-based person authentication. We compare performance results obtained with and without the second matching step. Results show that the deformation step slightly increases the performance, but has lower influence than the weighting of the nodes. The best results are obtained with the combination of both aspects. The results provided by the proposed method compare favorably with two methods that require a prior geometric face normalization, namely the synergetic and eigenface approaches.

Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is ...

We propose a new method to detect human faces in color images. A human skin color model is built to capture the chromatic properties based on multivariate statistical analysis. Given a color image, multiscale segmentation is used to generate homogeneous regions at multiple different scales. From the coarsest to the finest scale, regions of skin color are merged until the shape is approximately elliptic. Postprocessing is performed to determine whether a merged region contains a human face and include the facial features of non-skin color such as eyes and mouth if necessary. Experimental results show that human faces in color images can be detected regardless of size, orientation and viewpoint. 1 Introduction Face detection has many applications, including teleconferencing [2], face recognition [6], and gesture recognition [12]. The goal of face detection is to determine whether or not there is any human face in the image, and, if present, return its location and spatial extent. The t...

Face detection is a key problem in building automated systems that perform face recognition. A very attractive approach for face detection is based on multiresolution images (also known as mosaic images). Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed that extends the work of G. Yang and T.S. Huang. The proposed algorithm has been applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons. It has been found that the algorithm provides a correct facial candidate in all cases. However, the success rate of the detected facial features (e.g. eyebrows/eyes, nostrils/nose, and mouth) that validate the choice of a facial candidate is found to be 86.5 % under the most strict evaluation conditions. 1. INTRODUCTION Face recognition has been an active research topic in computer vision for more than two decades. A critical survey of the literature on human and ...

We describe an algorithm for detecting human faces and facial features, such as the location of the eyes, nose, and mouth. First, a supervised pixel-based color classifier is employed to mark all pixels that are within a prespecified distance of "skin color," which is computed from a training set of skin patches. This color-classification map is then smoothed by Gibbs random field model-based filters to define skin regions. An ellipse model is fit to each disjoint skin region. Finally, we introduce symmetry-based cost functions to search the center of the eyes, tip of nose, and center of mouth within ellipses whose aspect ratio is similar to that of a face. Face detection facial feature detection image segmentation shape classification Gibbs random fields 1 Introduction Automatic detection and recognition of faces from still images and video is an active research area. A complete facial image analysis system should be able to localize faces in a given image, identify and pin-point fac...

It is evident that the process of face recognition, by definition, should be based on the content of a face. The problem is: what is a "face"? Recently, a state-of-the-art statistics-based face recognition system, the PCA plus LDA approach, has been proposed [1]. However, the authors used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statistics-based technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, a hypothesis testing model is proposed. We then implement the above mentioned face ...

An efficient face detection algorithm which can detect multiple faces in cluttered environment is proposed. The proposed system consists of three main steps. In the first step, a morphology-based technique is devised to perform eye-analogue segmentation. Morphological operations are applied to locate eye-analogue pixels in the original image. Then, a labeling process is executed to generate the eye-analogue segments. In the second step, the previously located eye-analogue segments are used as guides to search for potential face regions. The last step of the proposed system is to perform face verification. In this step, every face candidate obtained from the previous step is normalized to a standard size. Then, each of these normalized potential face images is fed into a trained backpropagation neural network for identification. After all the true faces are identified, their corresponding poses are located based on the guidance of optimizing a cost function. The proposed face...

Human faces provide enormous information and a friendly interface in intelligent human computer interaction. This has motivated a very active research area on, among others, face recognition, face tracking, pose estimation, expression recognition and gesture recognition. However, most existing methods on these topics assume human faces in an image or a image sequence have been identied and localized. To build a fully automated system that analyzes information of human faces, it is essential to develop robust and eÆcient algorithms to detect human faces. Given a single or a sequence of images, the goal of face detection is to identify and locate human faces regardless of their positions, scales, orientations and lighting conditions. Such problem is challenging because human faces are highly non-rigid objects with a high degree of variability in size, shape, color and texture. The purpose of this paper is to give a critical survey of existing techniques on face detection which has attra...

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG sequences.

Video content classification and retrieval is a necessary tool in the current merging of entertainment and information media. With the advent of broadband networking, every consumer will have video programs available on-line as well as in the traditional distribution channels. Systems that help in content management have to discern between different categories of video in order to provide for fast retrieval. In this paper we present a novel method for video classification based on face and text trajectories. This is based on the observation that in different TV categories there are different face and text trajectory patterns. Face and text tracking is applied to arbitrary video clips to extract faces and text trajectories. We used Hidden Markov Models (HMM) to classify a given video clip into predefined categories, e.g., commercial, news, sitcom and soap. Our preliminary experimental results show classification accuracy of over 80% for HMM method on short video clips. This paper descri...