International Journal Papers

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Browsing International Journal Papers by Author "Aliradi, Rachid"

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    A novel descriptor (LGBQ) based on Gabor filters
    (Springer, 2023-12-23) Aliradi, Rachid; Ouamane , Abdelmalik
    Recently, many existing automatic facial verification methods have focused on learning the optimal distance measurements between facials. Especially in the case of learning facial features by similarity which can make the proposed descriptors too weak. To justify filling this gap, we have proposed a new descriptor called Local Binary Gabor Quantization (LGBQ) for 3/2D face verification based on Gabor filters and uses tensor subspace transformation. Our main idea is to binarize the responses of eight Gabor filters based on eight orientations as a binary code which is converted into a decimal number and combines the advantage of three methods: Gabor, LBP, and LPQ. These descriptors provide more robustness to shape variations in face parts such as expression, pose, lighting, and scale. To do this, we have chosen to merge two techniques which are multilinear whitened principal component analysis (MWPCA) and tensor exponential discriminant analysis (TEDA). The experimentation is using two publicly available databases, namely, Bhosphorus, and CASIA 3D face database. The results show the supremacy of our method in terms of accuracy and execution time compared to state-of-the-art methods.
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    DIEDA: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification
    (Springer, 2018-01-30) Aliradi, Rachid; Belkhir, Abdelkader; Ouamane, Abdelmalik; Elmaghraby , Adel S.
    Face and kinship verification using facial images is a novel and challenging problem in computer vision. In this paper, we propose a new system that uses discriminative information, which is based on the exponential discriminant analysis (DIEDA) combined with multiple scale descriptors. The histograms of different patches are concatenated to form a high dimensional feature vector, which represents a specific descriptor at a given scale. The projected histograms for each zone use the cosine similarity metric to reduce the feature vector dimensionality. Lastly, zone scores corresponding to various descriptors at different scales are fused and verified by using a classifier. This paper exploits discriminative side information for face and kinship verification in the wild (image pairs are from the same person or not). To tackle this problem, we take examples of the face samples with unlabeled kin relations from the labeled face in the wild dataset as the reference set. We create an optimized function by minimizing the interclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kinship relation) with the DIEDA approach. Experimental results on three publicly available face and kinship datasets show the superior performance of the proposed system over other state-of-the-art techniques.
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    Semantic indexing of multimedia content using textual and visual information
    (Inderscience, 2014) Amrane, Abdesalam; Mellah, Hakima; Amghar, Youssef; Aliradi, Rachid
    The challenge in multimedia information retrieval remains in the indexing process, an active search area. There are three fundamental techniques for indexing multimedia content: those using textual information, and those using low-level information and those that combine different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improve multimedia retrieval systems. The recent works are oriented towards multimodal approaches. In this paper we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated using the WordNet ontology in order to define its semantic concept. Support Vector Machines (SVMs) are used for image classification in one of the defined semantic concept based on SIFT (Scale Invariant Feature Transform) descriptors.