Meshes vs. Depth Maps in Face Recognition Systems

Pabiasz, Sebastian; Starczewski, Janusz T.

doi:10.1007/978-3-642-29347-4_66

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The promising approach to the learning problem which should be considered is using various types of computational intelligence systems [2,11,12,13,16,17,18,19,20,27,41,43,44] including neural networks, in particular convolutional neural networks [28]. Such systems and their parallel implementation on Intel MIC architecture can be useful in many applications, such as face recognition [33,34,35,36].…”

Section: Discussionmentioning

confidence: 99%

Adaptation of RBM Learning for Intel MIC Architecture

Olas

Mleczko

Nowicki

et al. 2015

Artificial Intelligence and Soft Computing

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Abstract. In the paper, the parallel realization of the Boltzmann Restricted Machine (RBM) is proposed. The implementation intends to use multicore architectures of modern CPUs and Intel Xeon Phi coprocessor. The learning procedure is based on the matrix description of RBM, where the learning samples are grouped into packages, and represented as matrices. The influence of the package size on convergence of learning, as well as on performance of computation, are studied for various number of threads, using conventional CPU and Intel Phi architecures. Our research confirms a potential usefulness of MIC parallel architecture for implementation of RBM and similar algorithms.

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Section: Discussionmentioning

confidence: 99%

Adaptation of RBM Learning for Intel MIC Architecture

Olas

Mleczko

Nowicki

et al. 2015

Artificial Intelligence and Soft Computing

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“…Its advantage is that the method of signature acquisition is not controversial, as in the case of certain biometric characteristics such as fingerprint or face image (see e.g. [47,48]). …”

Section: Introductionmentioning

confidence: 99%

New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values

Zalasiński

Cpałka

Hayashi

2015

Artificial Intelligence and Soft Computing

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Abstract. Identity verification based on the dynamic signature is an important issue of biometrics. There are many effective methods to the signature verification which take into account the dynamic characteristics of the signature (e.g. velocity of the pen, the pen's pressure on the surface of the graphic tablet, etc.). Among these methods, the ones based on the so-called global features are very important. In our previous paper we have proposed new algorithm for evolutionary selection of the dynamic signature global features, which selects a subset of features individually for each user. Algorithm proposed in this paper is a faster version of the method proposed earlier. During development of the algorithm we resigned from using evolutionary selection of global features and standardized working of the classifier in the context of all users. The paper contains the simulation results for the BioSecure database of the dynamic signatures.

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