Fast and efficient 3D face recognition using wavelet networks

Said, Salwa; Amor, Boulbaba Ben; Zaied, Mourad; Amar, Chokri Ben; Daoudi, Mohamed

doi:10.1109/icip.2009.5413446

Cited by 29 publications

(9 citation statements)

References 12 publications

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“…Since Beta wavelet [1] is a powerful tool in various domains such as image compression [2], face recognition [3,4], 3D face recognition [5], image classification [6,7], phoneme recognition [8], speech recognition [9] and in particular Arabic word recognition [10] and hand tracking and recognition [11]; this study used the Fast Beta Wavelet Network (FBWN) modeling to propose a new approach for CBIR.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Section: Introductionmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

Self Cite

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“…Similar to the wavelet network (Said et al, 2009), the Shearlet Network is a combination of a RBF neural network and the shearlet decomposition. During the optimization stage, each shearlet coefficient from the library is processed through the hidden layer of the network.…”

Section: B Sn For Features Extractionmentioning

confidence: 99%

Sparse multi-stage regularized feature learning for robust face recognition

Borgi

Labate

Arbi

et al. 2015

Expert Systems with Applications

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Abstract-The major limitation in current facial recognition systems is that they do not perform very well in uncontrolled environments, that is, when faces present variations in pose, illumination, facial expressions and environment. This is a serious obstacle in applications such as law enforcement and surveillance systems. To address this limitation, in this paper we introduce an improved approach to ensure robust face recognition, that relies on two innovative ideas. First, we apply a new multiscale directional framework, called Shearlet Network (SN), to extract facial features. The advantage of this approach comes from the highly sparse representation properties of the shearlet framework that is especially designed to robustly extract the fundamental geometric content of an image. Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition stage. We provide extensive numerical tests to show that our Sparse Multi-Regularized Shearlet Network (SMRSN) algorithm performs very competitively when compared against different state-of-the-art methods on different experimental protocols, including face recognition in uncontrolled conditions and single-sample-per-person.

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“…The Gallery faces are approximated by a shearlet network to produce a compact biometric signature as wavelet network [38]. One main feature of this approach is that this signature, constituted the shearlets and their weights, will be used to match a Probe with all faces in the Gallery.…”

Section: A Sn For Modeling and Features Extractionmentioning

confidence: 99%

ShearFace: Efficient Extraction of Anisotropic Features for Face Recognition

Borgi

Labate

Arbi

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Abstract-This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), that takes advantage of the sparse representation properties of shearlets in biometric applications. The main novelty of our approach is the efficient extraction of geometric features based on the properties of the shearlet decomposition, a multiscale directional method which is especially designed to capture directional and anisotropic information in multidimensional data. To further improve the performance of our face recognition algorithm, we include a regularization step to control the trade-off between the fidelity to the data (gallery) and smoothness of the solution (probe). In this work, we focus on the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-arts method using several facial databases including AR, FERET, FRGC, FEI and CK. Our tests show that our RSN algorithm is very competitive and outperforms several state-of-the-art face recognition methods.

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Fast and efficient 3D face recognition using wavelet networks

Cited by 29 publications

References 12 publications

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Sparse multi-stage regularized feature learning for robust face recognition

ShearFace: Efficient Extraction of Anisotropic Features for Face Recognition

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