Abstract-Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90 % correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach. Index Terms-Classification, face recognition, Karhunen-Loeve expansion, template matching.

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.

We describe a computer program which understands a greyscale image of a face well enough to locate individual face features such as eyes and mouth. The program has two distinct components: modules designed to locate particular face features, usually in a restricted area; and the overall control strategy which activates modules on the basis of the current solution state, and assesses and integrates the results of each module.

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...

An image of a face depends not only on its shape, but also on the viewpoint, illumination conditions, and facial expression. A face recognition system must overcome the changes in face appearance induced by these factors. This paper investigate two related questions: the capacity of the human visual system to generalize the recognition of faces to novel images, and the level at which this generalization occurs. We approach this problems by comparing the identification and generalization capacity for upright and inverted faces. For upright faces, we found remarkably good generalization to novel conditions. For inverted faces, the generalization to novel views was significantly worse for both new illumination and viewpoint, although the performance on the training images was similar to the upright condition. Our results indicate that at least some of the processes that support generalization across viewpoint and illumination are neither universal (because subjects did not generalize as e...

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This survey compares internal representations of the recent as well as more traditional face recognition techniques to classify them into several broad categories. The categories assessed include template matching and feature measurements, analysis of global and local facial features, and incorporation of interpersonal and intrapersonal variations of human faces. Analysis of the face recognition systems within those broad categories makes it possible to identify strong and weak sides of each group of methods. The paper argues that a fruitful direction for future research may lie in weighing information about facial features together with localized image features in order to provide a better mechanism for

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces. 1 Introduction Face recognition systems can be subdivided into two main categories [1] depending on the nature of the coding of an input picture and its processing. Schemes that use pixels (grey-level values) as the basis for their coding and various forms of sta...

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...

The paper describes a multisensorial person identification system: visual and acoustic cues are used jointly for person identification. A simple approach, based on the fusion of the lists of scores produced independently by a speaker recognition system and a face recognition system, is presented. Experiments are reported which show that integration of visual and acoustic information enhances both performance and reliability of the separate systems. Finally two network architectures, based on radial basis function theory, are proposed to describe integration at different levels of abstraction. Keywords: face recognition, speaker identification, classification 1. Introduction This paper describes an automatic person recognition system 1 which uses both acoustic features, derived from the analysis of a given speech signal, and visual ones, related to distinctive parameters of the face of the person who uttered that speech signal. Visual and acoustic cues are used jointly for person id...