Implementation of perceptual aspects in a face recognition algorithm
Automatic face recognition is a biometric technique particularly appreciated in security applications. In fact face recognition presents the opportunity to operate at a low invasive level without the collaboration of the subjects under tests, with face images gathered either from surveillance system...
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          | Published in | Journal of physics. Conference series Vol. 459; no. 1; pp. 12031 - 8 | 
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| Main Authors | , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Bristol
          IOP Publishing
    
        01.01.2013
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 1742-6588 1742-6596 1742-6596  | 
| DOI | 10.1088/1742-6596/459/1/012031 | 
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| Summary: | Automatic face recognition is a biometric technique particularly appreciated in security applications. In fact face recognition presents the opportunity to operate at a low invasive level without the collaboration of the subjects under tests, with face images gathered either from surveillance systems or from specific cameras located in strategic points. The automatic recognition algorithms perform a measurement, on the face images, of a set of specific characteristics of the subject and provide a recognition decision based on the measurement results. Unfortunately several quantities may influence the measurement of the face geometry such as its orientation, the lighting conditions, the expression and so on, affecting the recognition rate. On the other hand human recognition of face is a very robust process far less influenced by the surrounding conditions. For this reason it may be interesting to insert perceptual aspects in an automatic facial-based recognition algorithm to improve its robustness. This paper presents a first study in this direction investigating the correlation between the results of a perception experiment and the facial geometry, estimated by means of the position of a set of repere points. | 
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23  | 
| ISSN: | 1742-6588 1742-6596 1742-6596  | 
| DOI: | 10.1088/1742-6596/459/1/012031 |