Modelling face recognition.

Much early work in the psychology of face processing was hampered by a failure to think carefully about task demands. Recently our understanding of the processes involved in the recognition of familiar faces has been both encapsulated in, and guided by, functional models of the processes involved in processing and recognizing faces. The specification and predictive power of such theory has been increased with the development of an implemented model, based upon an 'interactive activation and competition' architecture. However, a major deficiency in most accounts of face processing is their failure to spell out the perceptual primitives that form the basis of our representations for faces. Possible representational schemes are discussed, and the potential role of three-dimensional representations of the face is emphasized.     

面孔识别中跨种族效应的形成及对策研究

面孔识别中的跨种族效应(crossr-race effect)是指人们对于本种族人面孔的识别和记忆成绩要好于时外种族人面孔的识别和记忆.认知和社会心理学近三十年来对跨种族效应的一系列研究证明了跨种族效应的存在和其强大的影响.目前关于跨种族效应产生的原因各个理论都从不同方面进行了解释,但是尚无定论.本文在已有文献的基础上,综述跨种族效应产生的原因及减少跨种族效应的对策.     

The significance of hair for face recognition

Hair is a feature of the head that frequently changes in different situations. For this reason much research in the area of face perception has employed stimuli without hair. To investigate the effect of the presence of hair we used faces with and without hair in a recognition task. Participants took part in trials in which the state of the hair either remained consistent (Same) or switched between learning and test (Switch). It was found that in the Same trials performance did not differ for stimuli presented with and without hair. This implies that there is sufficient information in the internal features of the face for optimal performance in this task. It was also found that performance in the Switch trials was substantially lower than in the Same trials. This drop in accuracy when the stimuli were switched suggests that faces are represented in a holistic manner and that manipulation of the hair causes disruption to this, with implications for the interpretation of some previous studies.     

A testing time for face recognition technology

Face recognition continues to make progress, with significant numbers of players now having commercial products. But the sector needs to address scalability, privacy and accuracy problems to become a truly dominant force in the industry.     

Quaternion-based discriminant analysis method for color face recognition

Pattern recognition techniques have been used to automatically recognize the objects, personal identities, predict the function of protein, the category of the cancer, identify lesion, perform product inspection, and so on. In this paper we propose a novel quaternion-based discriminant method. This method represents and classifies color images in a simple and mathematically tractable way. The proposed method is suitable for a large variety of real-world applications such as color face recognition and classification of the ground target shown in multispectrum remote images. This method first uses the quaternion number to denote the pixel in the color image and exploits a quaternion vector to represent the color image. This method then uses the linear discriminant analysis algorithm to transform the quaternion vector into a lower-dimensional quaternion vector and classifies it in this space. The experimental results show that the proposed method can obtain a very high accuracy for color face recognition.