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Automatic face recognition using SIFT and networks of tagged neural cliques

Abstract : Bearing information by a fully interconnected subgraphs is recently improved in the neural network of cliques. In this paper, a face recognition system is presented using such networks where local descriptors are used to perform feature extraction. In the wide range of possible image descriptors for face recognition, we focus specifically on the Scale Invariant Feature Transform (SIFT). In contrast to standard methods, our proposed method requires no empirically chosen threshold. Moreover, it performs matching between sets of features, in addition to individual feature matching. Thus, we favor joint occurrences of descriptors during the recognition process. We compare our approach to state of the art face recognition systems based on SIFT descriptors. The evaluation is carried out on the Olivetti and Oracle Research Laboratory (ORL) face database, whose diversity is significant for assessing face recognition methods.
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Submitted on : Tuesday, April 30, 2019 - 4:36:53 PM
Last modification on : Monday, October 11, 2021 - 2:23:51 PM


  • HAL Id : hal-02116078, version 1


Vincent Gripon, Ehsan Sedgh Gooya, Dominique Pastor. Automatic face recognition using SIFT and networks of tagged neural cliques. International Conference on Advanced Cognitive Technologies and Applications 2015, Mar 2015, Nice, France. pp.57 - 61. ⟨hal-02116078⟩



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