Face recognition: features versus templates

Brunelli, Roberto; Poggio, Tomaso

doi:10.1109/34.254061

Cited by 2,064 publications

(924 citation statements)

References 17 publications

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“…It suggests that at least under conditions of low resolution, it is not the internal or external configurations on their own that subserve recognition, but rather measurements corresponding to how internal features are placed relative to the external features. This idea conflicts with conventional notions of facial configuration, especially prominent in the computational vision community, which primarily involve 'internal' measurements such as inter-eye, eye to nose-tip and nose-tip to mouth distances (20,21). Thus, external features, even though poor indicators of identity on their own, provide an important frame of reference for analyzing facial configuration.…”

Section: Discussionmentioning

confidence: 95%

Relative Contributions of Internal and External Features to Face Recognition

Jarudi¹,

Sinha²

2003

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CBCL Memo 225© 2 0 0 3 m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y, c a m b r i d g e , m a 0 2 1 3 9 u s a -w w w. a i . m i t . e d u m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y -a r t i f i c i a l i n t e l l i g e n c e l a b o r a t o r y @ MIT 1 AbstractThe central challenge in face recognition lies in understanding the role different facial features play in our judgments of identity. Notable in this regard are the relative contributions of the internal (eyes, nose and mouth) and external (hair and jaw-line) features. Past studies that have investigated this issue have typically used high-resolution images or good-quality line drawings as facial stimuli. The results obtained are therefore most relevant for understanding the identification of faces at close range. However, given that real-world viewing conditions are rarely optimal, it is also important to know how image degradations, such as loss of resolution caused by large viewing distances, influence our ability to use internal and external features. Here, we report experiments designed to address this issue. Our data characterize how the relative contributions of internal and external features change as a function of image resolution. While we replicated results of previous studies that have shown internal features of familiar faces to be more useful for recognition than external features at high resolution, we found that the two feature sets reverse in importance as resolution decreases. These results suggest that the visual system uses a highly non-linear cue-fusion strategy in combining internal and external features along the dimension of image resolution and that the configural cues that relate the two feature sets play an important role in judgments of facial identity.

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Section: Discussionmentioning

confidence: 95%

Relative Contributions of Internal and External Features to Face Recognition

Jarudi¹,

Sinha²

2003

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“…The use of neural networks is an alternative to the use of other classiÿers once a feature vector has been obtained. Other neural architectures have also been used for face recognition, as HyperBF networks [10].…”

Section: Face Recognitionmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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“…Under this condition, most of the traditional methods such as eigenface [3] and fisherface [4] will suffer serious performance drop or even fail to work, due to the absence of enough samples for a reliable covariation estimation. This problem, called the one sample per person problem (or, one sample problem for short), is defined as follows: Given a stored database of faces with only one image per person, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from the individual image.…”

Section: Introductionmentioning

confidence: 99%

Recognition from a Single Sample per Person with Multiple SOM Fusion

Tan

Jun

Chen

2006

Advances in Neural Networks - ISNN 2006

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Abstract. One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples, and many existing face recognition techniques rely heavily on the size and representative of training set. Those algorithms may suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. In this paper, we present a multiple-SOMs-based fusion method to address this problem. Based on the localization of the face, multiple Self-Organizing Maps are constructed in different manners, and then fused to obtain a more compact and robust representation of the face, through which the discrimination and class-specific information can be easily explored from the single training image among a large number of classes. Experiments on the FERET face database show that the proposed fusion method can significantly improve the performance of the recognition system, achieving a top 1 matching rate of 90.0%.

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Face recognition: features versus templates

Cited by 2,064 publications

References 17 publications

Relative Contributions of Internal and External Features to Face Recognition

Relative Contributions of Internal and External Features to Face Recognition

Biometric identification systems

Recognition from a Single Sample per Person with Multiple SOM Fusion

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