International Conference Papers

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Browsing International Conference Papers by Author "Aliradi, Rachid"

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    BSIF Features Learning using TXQEDA Tensor Subspace for kinship verification
    (Echahid Cheikh Larbi Tebessi university, 2023-06) Aliradi, Rachid; Ouamane , Abdealmalik
    Facial kinship verification is a hard research domain in vision that has very interesting regard in the latest decennial. Various applications were really realized in social media, biometrics, and development in studies of demographic. But the result accuracies obtained that is so weak to predict kinship relationships by facial appearance. To take up this challenge and tackle this problem. We use a new approach called Color BSIF learning an approach that has appeared as an encouraging solution. The aim is to solve problem KV by using the color BSIF learning features with the TXQEDA method for dimensionality reduction and data classification in order to train the model, Let's test the kinship facial verification application namely the Cornell Kinface database. This framework ameliorates the time cost and efficiency. The experimental results obtained surpass other states of the art methods
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    Face and kinship image based on combination descriptors-DIEDA for large scale features
    (IEEE, 2018-12-30) Aliradi, Rachid; Belkhir, Abdelkader; Ouamane, Abdelmalik; Aliane, Hassina
    In this paper, we introduce an efficient linear similarity learning system for face verification. Humans can easily recognize each other by their faces and since the features of the face are unobtrusive to the condition of illumination and varying expression, the face remains as an access of active recognition technique to the human. The verification refers to the task of teaching a machine to recognize a pair of match and non-match faces (kin or No-kin) based on features extracted from facial images and to determine the degree of this similarity. There are real problems when the discriminative features are used in traditional kernel verification systems, such as concentration on the local information zones, containing enough noise in non-facing and redundant information in zones overlapping in certain blocks, manual adjustment of parameters and dimensions high vectors. To solve the above problems, a new method of robust face verification with combining with a large scales local features based on Discriminative-Information based on Exponential Discriminant Analysis (DIEDA). The projected histograms for each zone are scored using the discriminative metric learning. Finally, the different region scores corresponding to different descriptors at various scales are fused using Support Vector Machine (SVM) classifier. Compared with other relevant state-of-the-art work, this system improves the efficiency of learning while controlling the effectiveness. The experimental results proved that both of these two initializations are efficient and outperform performance of the other state-of-the-art techniques.
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    Indexing multimedia content for textual querying: A multimodal approach
    (2013-07) Amrane, Abdesalam; Mellah, Hakima; Amghar, Youssef; Aliradi, Rachid
    Multimedia retrieval approaches are classified into three categories: 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 improving multimedia retrieval systems. The recent works are oriented towards multimodal approaches. It is in this context that 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 by using the WordNet in order to define its semantic concept.