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.

AbstractÐThe Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.

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.

Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction. The Facial Action Coding System ~Ekman & Friesen, 1978! is an objective method for quantifying facial movement in terms of component actions. We applied computer image analysis to the problem of automatically detecting facial actions in sequences of images. Three approaches were compared: holistic spatial analysis, explicit measurement of features such as wrinkles, and estimation of motion flow fields. The three methods were combined in a hybrid system that classified six upper facial actions with 91 % accuracy. The hybrid system outperformed human nonexperts on this task and performed as well as highly trained experts. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.

Multi-modal person identity authentication is gaining more and more attention in the biometrics area. Combining different modalities increases the performance and robustness of identity authentication systems. The authentication problem is a binary classification problem. The fusion of different modalities can be therefore performed by binary classifiers. We propose to evaluate different binary classification schemes (SVM, MLP, C4.5, Fisher's linear discriminant, Bayesian classifier) on a large database (295 subjects) containing audio and video data. The identity authentication is based on two modalities: face and speech.

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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.

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

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.

ly, we applied principal component analysis to the pixel-coded face images with the aim of extracting measures related to the gender classifiability and recognizability of individual faces. We incorporated these model-derived measures into the factor analysis with the human rating and performance measures. This combined analysis indicated that face recognizability is related to the distinctiveness of a face with respect to its gender subcategory prototype. Additionally, the gender classifiability of faces related to at least one caricatured aspect of face gender. 1. introduction 1 Human faces provide us with a plethora of information that is valuable and necessary for social interaction. When we encounter a face, we can quickly and efficiently decide whether it is one we know. For faces of persons we know, we can often retrieve semantic and identity information about the person. Additionally, from both familiar and unfamiliar faces we can make judgments