Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

this paper we focus on featureextraction and face-identification processes

Human faces provide a useful cue in indexing video content. In this paper, we present a highly efficient system that can rapidly detect human face regions in MPEG video sequences. The underlying algorithm takes the inverse quantized DCT coefficients of MPEG video as the input, and outputs the locations of the detected face regions. The algorithm consists of three stages, where chrominance, shape, and frequency information are used respectively. By detecting faces directly in the compressed domain, there is no need to carry out the inverse DCT transform, so that the algorithm can run faster than the real time. In our experiments, the algorithm detected 85-92 % of the faces in three test sets, including both intra-frame and inter-frame coded image frames from news video. The average run time ranges from 13 to 33 milliseconds per frame. The algorithm can be applied to JPEG unconstrained images or motion JPEG video as well.

The work reported in this paper addresses the issue of automatically tracking the faces and facial features of persons in head-and-shoulders video sequences. We propose two totally automatic algorithms which respectively perform the detection of head outlines and identify rectangular "eyes-nose-mouth" regions, both from downsampled binary thresholded edge images. Unlike ones that have been proposed recently, a priori assumptions regarding the nature and content of the sequences to code are minimal for our techniques, and the algorithms operate accurately and robustly, even in cases of significant head rotation or partial occlusion by moving objects. 1 Introduction The motivation for this work was to investigate the feasability of detecting and tracking specific moving objects known a priori to be present in a video sequence, and to enable a low bit rate video coding system to use this information in order to discriminatively encode different areas in "head-and-shoulders" video sequen...

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...

This thesis considers segmentation and identification of human faces from grey scale images with clutter. The segmentation developed utilizes the elliptical structure of the human head. It uses the information present in the edge map of the image and through some preprocessing separates the head from the background clutter. An ellipse is then fitted to mark the boundary between the head region and the background. The identification procedure finds feature points in the segmented face through a Gabor wavelet decomposition and performs graph matching. The segmentation and identification algorithms were tested on a database of 48 images of 16 persons with encouraging results. The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Topographic Engineering Center under Contract DACA76-92-C-0009 is gratefully acknowledged, as is the help of Sandy German in preparing this paper. Dedication To my grandmother Masooda Begum and my parents Iftikhar A. Sir...

Control of a computer workstation via face position and facial gesturing would be an important advance for people with hand or body disabilities as well as for all users. Steps toward realization of such a system are reported here. A computer system has been developed to track the eyes and the nose of a subject and to compute the direction of the face. Face direction and movement is then used to control the cursor. Test results show that the resulting system is usable, although several improvements are needed. I. INTRODUCTION We demonstrate a mode of communication whereby the user controls the cursor and selects a desired menu item with his/her face. Our primary goal is to enhance both the work and living environment of disabled persons, but we believe that development of a system viable for all persons would have the most impact. Eye-tracking systems contained in helmets, glasses, or other devices have already been built to aim guns in aircraft and to study the scanning process of re...

In this article, we present an original approach for the localization of objects in an image. Our approach is neuronal and it includes two steps. In the first step, a rough localization is performed by presenting each pixel with its neighbourhood to a neural net which is able to indicate if this pixel and its neighbourhood are the image of the search object. This first filter is not very discriminant for the position. From its result, we can select areas which might contain an image of the object. In the second step, these areas are presented to another neural net which can determine the exact position of the object in each area. We apply this algorithm to the problem of localizing faces in images. 1 Introduction The detection and localization of faces in an image has many applications in various domains: surveillance, TV audience polling: : : . We propose a new method for this task which ffl does not require any hypothesis on the position of the face in the image, or on its scale, ...

A document image is a visual representation of a printed page such as a journal article page, a facsimile cover page, a technical document, an o#ce letter, etc. Document understanding as a research endeavor consists of studying all processes involved in taking a document through various representations: from a scanned physical document to high-level semantic descriptions of the document. Some of the types of representation that are useful are: editable descriptions, descriptions that enable exact reproductions and high-level semantic descriptions about document content. This report is a de#nition of #ve research subdomains within document understanding as pertaining to predominantly printed documents. The topics described are: modular architectures for document understanding; decomposition and structural analysis of documents; model-based OCR; table, diagram and image understanding; and performance evaluation under distortion and noise. 1 Each of the main sections of this paper were ...