Conference Paper

Predicting experimental similarity ratings and recognition rates for individual natural stimuli with the NIM model.

Conference: BNAIC 2005 - Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence, Brussels, Belgium, October 17-18, 2005
Source: DBLP

ABSTRACT In earlier work, we proposed a recognition memory model that operates directly on digitized natural images. The model is called the Natural Input Memory (NIM) model. When presented with a natural image, the NIM model employs a biologically-informed perceptual pre-processing method that translates the image into a similarity-space representation. In this paper, the NIM model is validated on individual natural stimuli (i.e., images of faces) in two tasks: (1) a similarity- rating task and (2) a recognition task. The results obtained with the NIM model are compared with the results of corre- sponding behavioral experiments. The similarity structure of the face images that is reflected in the similarity space forms the basis for the comparison. The results reveal that the N IM model's similarity ratings and recognition rates for individual images correlate well with those obtained in the behavioral ex- periments. We conclude that the NIM model successfully sim- ulates similarity ratings and recognition performance for indi- vidual natural stimuli.

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    ABSTRACT: Authentication codes such as passwords and PIN numbers are widely used to control access to resources. One major drawback of these codes is that they are difficult to remember. Account holders are often faced with a choice between forgetting a code, which can be inconvenient, or writing it down, which compromises security. In two studies, we test a new knowledge-based authentication method that does not impose memory load on the user. Psychological research on face recognition has revealed an important distinction between familiar and unfamiliar face perception: When a face is familiar to the observer, it can be identified across a wide range of images. However, when the face is unfamiliar, generalisation across images is poor. This contrast can be used as the basis for a personalised 'facelock', in which authentication succeeds or fails based on image-invariant recognition of faces that are familiar to the account holder. In Study 1, account holders authenticated easily by detecting familiar targets among other faces (97.5% success rate), even after a one-year delay (86.1% success rate). Zero-acquaintance attackers were reduced to guessing (<1% success rate). Even personal attackers who knew the account holder well were rarely able to authenticate (6.6% success rate). In Study 2, we found that shoulder-surfing attacks by strangers could be defeated by presenting different photos of the same target faces in observed and attacked grids (1.9% success rate). Our findings suggest that the contrast between familiar and unfamiliar face recognition may be useful for developers of graphical authentication systems.
    PeerJ. 01/2014; 2:e444.

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