LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

Ingénierie des exigences pour les processus inter-organisationnels

TOULOUSE2-BUC Mirail (315552102) / SudocSudocFranceF

A Driven-Context Composition mechanism for Mobile Applications: Metamodeling Approach

International audienceNowadays, most human activities rely on the use of mobile devices. The recent massive adoption of this technology explains the increasing demand for specific software. In spite of the large number of available mobile applications (apps) with their different implementation forms, the user's requirements differ from one to another. To tackle this issue and also to cope with the heterogeneous settings offered by the mobile devices available, there is a need for a composition mechanism to take advantage from existing services for developing mobile apps according to user's needs and adaptive to their execution environment. Several composition mechanisms were developed in order to meet the user's requirements using existing software entities. However, these existing approaches do not have a global vision of mobile apps composition (e.g. limiting the composition objects to a one type: components only, services only…etc.). In this paper, we propose a composition mechanism for developing context-aware mobile apps. It enables the composition of existing software entities independently from their implementation platforms (i.e. reusing homogeneous or heterogeneous software entities). It also aims to customize the behavior of the desired mobile app according to the various contextual information of the mobile device. To achieve this goal we follow a metamodeling approach. We propose description languages to define the desired mobile app at several abstract levels and we implement the passage among these descriptions using transformation mechanisms. A case study is presented to illustrate the applicability and the effectiveness of this approach

New approach for smart composition of mobile applications,

International audienc

An Automated Finger-Knuckle-Print Identification System Using Jointly RBF & RFT Classifiers

Ensuring the security of a wide variety of applications such as public security, access control and banking, is becoming an increasingly important problem as modern technology is integrated into the majority of these applications. Thus, establishing identity of a person is the significant method used to ensure a high security level. As a security method, biometric identification has a long tradition and is a synonym for the uniqueness of person. In this paper, based on Finger-Knuckle-Print (FKP), we present a multimodal personal identification system using two feature extraction methods with their fusion applied at the matching score level. In this study, the segmented FKP is firstly represented by the Histogram of Oriented Gradients(HOG) descriptors. Subsequently, the Radial Basis Function(RBF) and Random Forest Transform (RFT) models are used to design two sub-systems. The proposed method is validated for their efficacy on the available PolyU FKP Database of 165 users. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate

Selection algorithm of contextual software entities for composing adaptive mobile applications

International audiencethe development of customized mobile applications basing on the composition mechanism (i.e. using existing software entities) has received a lot of attention in the last couple of years. The mobile devices heterogeneity shows that the portability requirements play an important role in the mobile applications developpement domain. Otherwise, mobile applications strongly depend on the execution environment features. Thereby, in order to make sure the correct deployment and the proper functioning of the composite mobile application it is necessary to ensure that their constituents are adaptable to the current context of the mobile device. To cope with this issue and due to the fact that several software entities can be used to implement the identified requirements for a desired mobile application, we propose in this paper a context-driven selection algorithm that aims at selecting the adaptive software entities among all corresponding ones. Also, it targets to determine the different possible composition paths to build customized mobile applications. To achieve this objective, we propose ontology based descriptions to define the context of the corresponding software entities and the execution environment