In real-life applications, the appearance of a face changes significantly due to variations in expression, lighting, aging, exposure, and occlusion, which makes face recognition difficult. We present in this article a new approach for facial recognition. This approach is based on a set of variants of the Ho-LBP descriptor that we have proposed. In fact, the presentation of the images using a set of variants of the Ho-LBP descriptor helps the classifier to learn better. In addition, these variants are combined to improve the performance of facial recognition. We evaluated the effectiveness of our approach on ORL, Extended Yale B, and Feret databases. The obtained results are very promising, especially when compared with those of existing approaches. They show that our approach improves the accuracy of facial recognition in a very efficient way and in particular to the variations of the poses and the changes of the luminance.
We present a new approach for concurrency control over XML documents. Unlike most of other approaches, we use an optimistic scheme, since we believe that it is better suited for Web applications. The originality of our solution resides in the fact that we use path expressions associated with operations to detect conflicts between transactions. This makes our approach scalable since conflict detection except in few cases does not depend on the database size nor on the amount of modified fragments. In this paper, we describe and motivate our concurrency mechanism architecture, we describe the conflict detection algorithm which is the core of our proposal and exhibit first experimental results.
In recent years, several descriptors have been proposed in many image classification applications. Accelerated-KAZE (A-KAZE) is considered one of the descriptors that has shown high performance for feature extraction. A-KAZE uses a binary descriptor called modified-local difference binary, which is very efficient and invariant to changes in rotation and scale. This representation does not allow spatial information to be considered between objects in the image, which makes it possible to reduce the performances of the classification of the images. This article broaches a new approach to improve the performance of the A-KAZE descriptor for image classification. The authors first establish the connection between the A-KAZE descriptor and the bag of feature model. Then the Spatial Pyramid Matching (SPM) is adopted by exploiting the A-KAZE descriptor to reinforce its robustness by introducing spatial information. The results of the experiments on several datasets show that the A-KAZE descriptor with SPM gives very satisfactory results compared with other existing methods in the state of the art.
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