A new approach of hybrid decision tree based floor state recognition

International audienceBlind people need some aid to interact with their environment with more security, especially, to avoid collision and falling. The process of staircase negotiation is complex for visually impaired people. Therefore, an intelligent system is worth helping them. In this paper, we investigate incorporating only one ultrasonic sensor within an electronic white cane to recognize floor state and classify the environment in three classes, even surface, ascending stair case and descending stair case. In our knowledge, no previous work was concerned with such context. The performance of floor state recognition system depends, firstly, on signal processing strategy, then, on objects representation and, finally, on classification approach, making the decision appropriate. In this paper, the proposed strategy consists in merging more than one transformation of ultrasonic signals, mostly Fourier transform and wavelet transform, used as features in a hybrid decision tree

Possibilistic modeling of iris system for high recognition reliability

International audienceThe human iris is attracting a lot of attention leading to an increased demand for a robust and reliable iris-based recognition system. However, typical systems have random performances which can be efficient for some contexts and not for others. Generally, in real-world applications, iris data imperfections affect the performance of the recognition algorithms. Possibilistic modeling is a powerful tool to handle data imperfections or redundancy without being affected by data variability. Therefore, in this paper, we propose a new method for biometric recognition of human irises based on possibilistic modeling approach. The proposed approach is based on possibility theory concepts for modeling iris features in a possibilistic space. Therefore, a set of iris features, extracted from image samples, is transformed into possibility distributions for possibilistic iris representation. Features in the possibilistic space allow better recognition rates over conventional features. Validation of the proposed method was done on four challenging CASIA iris image databases namely Thousand, Interval, Twins and Synthetic. For illustration, we consider a typical iris system, from the literature. Such a system leads to an Accuracy Recognition Rate (ARR) of 72.27% on the CASIA Thousand iris database, an ARR of 78.06% on the CASIA Interval database, an ARR of 71.88% on the CASIA twins and an ARR of 77.35% on the CASIA synthetic database. Possibilistic modeling of features leads to an improvement of the ARR which is respectively of 99.92%, 99.94%, 99.91% and 99.83% for the considered databases. Possibilistic modeling leads as well to a significant reduction of the Error Equal Error Rate (EER). Indeed the typical system leads to an EER of 27.72%, 21.93%, 28.11% and 22.64%, respectively on the CASIA Thousands, Interval, Twins and synthetic databases. whereas our proposed system leads to EER of 0.04%, 0.05%, 0.08% and 0.16%, respectively for the above mentioned databases. Besides, the proposed method improves the system performance with respect to False Acceptance Rate (FAR), False rejection Rate (FRR), Area Under the receiver operating characteristics Curve (AUC)

Possibilistic modeling of iris system for high recognition reliability

International audienceThe human iris is attracting a lot of attention leading to an increased demand for a robust and reliable iris-based recognition system. However, typical systems have random performances which can be efficient for some contexts and not for others. Generally, in real-world applications, iris data imperfections affect the performance of the recognition algorithms. Possibilistic modeling is a powerful tool to handle data imperfections or redundancy without being affected by data variability. Therefore, in this paper, we propose a new method for biometric recognition of human irises based on possibilistic modeling approach. The proposed approach is based on possibility theory concepts for modeling iris features in a possibilistic space. Therefore, a set of iris features, extracted from image samples, is transformed into possibility distributions for possibilistic iris representation. Features in the possibilistic space allow better recognition rates over conventional features. Validation of the proposed method was done on four challenging CASIA iris image databases namely Thousand, Interval, Twins and Synthetic. For illustration, we consider a typical iris system, from the literature. Such a system leads to an Accuracy Recognition Rate (ARR) of 72.27% on the CASIA Thousand iris database, an ARR of 78.06% on the CASIA Interval database, an ARR of 71.88% on the CASIA twins and an ARR of 77.35% on the CASIA synthetic database. Possibilistic modeling of features leads to an improvement of the ARR which is respectively of 99.92%, 99.94%, 99.91% and 99.83% for the considered databases. Possibilistic modeling leads as well to a significant reduction of the Error Equal Error Rate (EER). Indeed the typical system leads to an EER of 27.72%, 21.93%, 28.11% and 22.64%, respectively on the CASIA Thousands, Interval, Twins and synthetic databases. whereas our proposed system leads to EER of 0.04%, 0.05%, 0.08% and 0.16%, respectively for the above mentioned databases. Besides, the proposed method improves the system performance with respect to False Acceptance Rate (FAR), False rejection Rate (FRR), Area Under the receiver operating characteristics Curve (AUC)

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS

International audienceIn this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS’s highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5–6% range

Feature selection in possibilistic modeling

International audienceFeature selection is becoming increasingly important for the reduction of computing complexity. In this context, conventional approaches have random performances, because They can succeed for some contexts and fail for others. Possibilistic modeling is a powerful paradigm being able to handle data imperfection or redundancy and is not affected by data variability. Therefore, in this paper, we propose a new feature selection strategy for possibilitic modeling. The proposed approach is based on two issues in order to extract relevant features: the measure of feature importance as well as the possibility distribution uncertainty degree. The importance of one feature can be considered under two aspects: The first one is related to the scattering within one class and the second one reflects the feature power for class discrimination. Therefore, we apply, here, Shapley index paradigm which selects features who minimize the intra-class distance and who maximize the inter-class distance. The previous process is refined using possibility distribution uncertainty degree in order to resolve some conflict problems between feature׳s importance values

Feature selection based on discriminative power under uncertainty for computer vision applications

Feature selection is a prolific research field, which has been widely studied in the last decades and has been successfully applied to numerous computer vision systems. It mainly aims to reduce the dimensionality and thus the system complexity. Features have not the same importance within the different classes. Some of them perform for class representation while others perform for class separation. In this paper, a new feature selection method based on discriminative power is proposed to select the relevant features under an uncertain framework, where the uncertainty is expressed through a possibility distribution. In an uncertain context, our method shows its ability to select features that can represent and discriminate between classes