Browsing by Author "Chibani, Youcef"

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Feature Fusion for Kinship Verification based on Face Image Analysis
(University of Eloued جامعة الوادي, 2022-01-24) Zekrini, Fatima; Nemmour, Hassiba; Chibani, Youcef
This paper proposes the fusion of two new features for improving kinship verification based on face image analysis. Combined features are the Gradient Local Binary Patterns (GLBP), which associates gradient and textural information. The second descriptor is the Histogram Of Templates (HOT), which is a shape descriptor. These features are utilized with the support vector machines classifier to develop the kinship verification. Experiments are carried out on Cornell and Kinface W-II datasets. Results obtained highlight the effectiveness of the proposed system which provide competitive and sometimes better performance than the state of the art.
Feature Selection using F-score Method for Offline Arabic Handwritten Fragment Identification
(University of Eloued جامعة الوادي, 2022-01-24) Azzoug, Soraya; Chibani, Youcef; Djoudjai, Mohamed Anis
Among the various means of document authentication, handwriting fragments represent one of the challenging tasks for automatic handwritten writer identification. Usually, the writer is represented by a set of features extracted for each handwritten fragment. However, feature components might be irrelevant due to their redundancy, affecting the pertinence of the feature vector. Hence, this paper proposes a feature-selection strategy based on a hybrid F-score method, performed genuinely for multi-class classification. The F-score is used conjointly to a classifier based-distance. According to a statistical analysis performed on F-score distribution during the training step, the number of pertinent feature components is deduced from the highest F-score value. The experimental evaluation performed on the well-known IFN/ENIT handwritten fragment dataset shows an improvement of the identification rate of 94.20% while reducing the size of the feature vector of 54%.
Interval versus Histogram of Symbolic Representation Based One-Class Classifier for Offline Handwritten Signature Verification
(University of Eloued جامعة الوادي, 2022-01-24) Djoudjai, Mohamed Anis; Chibani, Youcef
This paper proposes a comparison study of using Interval and Histogram of Symbolic Representation (ISR and HSR) based One-Class classifiers, namely OC-ISR and OC-HSR, respectively, applied to the offline signature verification. Usually, symbolic verification models are built straightforward from the feature space. The proposed work explores an alternative approach based on the use of feature-dissimilarities generated from Curvelet Transform (CT) for building the OC-ISR and the OC-HSR classifier. For the OC-ISR classifier, a new weighted membership function is proposed for computing the similarity values between a dissimilarity query vector and a targeted ISR model. The experimental evaluation performed on the well-known public datasets GPDS, CEDAR, and MCYT, reveals the proposed OC-ISR's superiority over the OC-HSR classifier. Moreover, the proposed verification model based on the OC-ISR classifier outperforms the last similar work reported in the literature on the GPDS-160 dataset by 0.99%, 0.8%, and 0.35% of Average Error Rate (AER) for 5, 8, and 12 reference signatures, respectively.
On the use of the convolutional autoencoder for Arabic writer identification using handwritten text fragments
(University of Eloued جامعة الوادي, 2022-01-24) Briber, Amina; Chibani, Youcef
Convolutional autoencoders (CAE) are designed to reconstruct the input image to the output in a near-perfect way via a compact data namely encoded data containing relevant features. The encoded data can be used in various applications as for compressing or classifying the image. The present paper tries to investigate the use of the CAE for writer identification using handwritten text fragments. Hence, the CAE is used for generating features, which is fed to the distance-based classifier. Experimental evaluation is performed on the wellknown IFN/ENIT dataset containing 411 writers. During training, a subset is selected from the 411 writers containing only 11 writers allowing to produce a lite CAE. Experimental results show an identification rate of 92.70% using the whole dataset when the feature vector is appropriately normalized.