Researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing human faces for the last 20 years. While some systems, especially template-based ones, have been quite successful on expressionless, frontal views of faces with controlled lighting, not much work has taken face recognizers beyond these narrow imaging conditions. Our goal is to build a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. To recognize a novel view, the recognizer locates the eyes and nose features, uses these locations to geometrically register the input with model views, and then uses correlation on model templates to find the best match in the data base of people. Our system has achieved a recognition rate of 98% on a data base...

Abstract—An automatic personal identification system based solely on fingerprints or faces is often not able to meet the system performance requirements. Face recognition is fast but not extremely reliable, while fingerprint verification is reliable but inefficient in database retrieval. We have developed a prototype biometric system which integrates faces and fingerprints. The system overcomes the limitations of face recognition systems as well as fingerprint verification systems. The integrated prototype system operates in the identification mode with an admissible response time. The identity established by the system is more reliable than the identity established by a face recognition system. In addition, the proposed decision fusion scheme enables performance improvement by integrating multiple cues with different confidence measures. Experimental results demonstrate that our system performs very well. It meets the response time as well as the accuracy requirements. Index Terms—Biometrics, fingerprint matching, minutiae, face recognition, eigenface, decision fusion.

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 ...

Several different techniques have been proposed for computer recognition of human faces. This paper presents the first results of an ongoing project to compare several recognition strategies on a common database. A set of algorithms has been developed to assess the feasibility of recognition using a vector of geometrical features, such as nose width and length, mouth position and chin shape. The performance of a Nearest Neighbor classifier, with a suitably defined metric, is reported as a function of the number of classes to be discriminated (people to be recognized) and of the number of examples per class. Finally, performance of classification with rejection is investigated.

The paper proposes a novel, pose-invariant face recogI#TfA system based on a deformable,geform 3D face model, that is a composite of: (1) anedg model, (2) a color regrf model and (3) a wireframe model for jointlydescribing the shape and important features of the face. The #rst two submodels are used forimag analysis and the third mainly for face synthesis. In order to match the model to faceimagy in arbitrary poses, the 3D model can be projected onto di#erent 2D viewplanes based on rotation, translation and scale parameters, therebygrebyf:Ik multipleface-imag templates (in di#erent sizes and orientations). Face shape variationsamong people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by modelmatching and the system synthesizes faceimagj of known subjects in the same pose. The face is then classi#ed as the subject whose synthesizedimag is most similar. The synthesizedimagh are gref#k#j using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived fromtraining faceimag: of the subject. Experimental results show that the method is capable ofdetermining pose and recog##fA: faces accurately over a wide rang ofposes and with naturallyvarying liging conditions. Recogions. rates of92.3% have been achieved by the method with 10training faceimagk per person.

A biologically motivated compute intensive approach to computer vision is developed and applied to the problem of face recognition. The approach is based on the use of twodimensional Gabor functions that fit the receptive fields of simple cells in the primary visual cortex of mammals. A descriptor set that is robust against translations is extracted by a global reduction operation and used for a search in an image database. The method was applied on a database of 205 face images of 30 persons and a recognition rate of 94% was achieved.

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called “one sample per person ” problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.

A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting -- a common problem in high dimensional classification schemes -- are presented, and the superiority of their combination is demonstrated. Key words: Face recognition, Neural Networks, Interest points, Symmetry operator. To appear: Pattern Recognition Letters 17 (1996) 67-76 1 Introduction Automatic face recognition has gained much attention in recent years, due to the variety of potential applications, and the increase in computational power which enables effective implementation of algorithms. Traditionally, face recognition was based on extracting certain features (e.g. spatial location of facial features and their geometrical relations) [4, 20]....

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Content-based indexing methods are of great interest for image and video retrieval in audio-visual archives, such as in the Esprit project DiVAN that we are currently developing. Detecting and recognizing human faces automatically in video data provide users with powerful tools for performing queries. In this article, a new scheme for face recognition using a wavelet packet decomposition is presented. Each face is described by a subset of band filtered images containing wavelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. Then, an efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the face feature vectors into person classes. Keywords: Face recognition; Wavelet decomposition; Bhattacharrya distance 1 Introduction Face recognition is becoming a very promising tool for automatic multimedia content analysis an...