Imad Rida

Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition

Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.

Gait Recognition Based on Modified Phase Only Correlation

Clothing, carrying conditions, and other intra-class variations, also referred as ”covariates”, affect the performance of gait recognition systems. This paper proposes a supervised feature extraction method which is able to select relevant features for human recognition to mitigates the impact of covariates and hence improve the recognition performance. The proposed method is evaluated using CASIA Gait Database (Dataset B) and the experimental results suggest that our method yields attractive results.

Palmprint Identification Using an Ensemble of Sparse Representations

Among various palmprint identification methods proposed in the literature, sparse representation for classification (SRC) is very attractive offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In particular, SRC suffers from two major problems: lack of training samples per class and large intra-class variations. In fact, palmprint images not only contain identity information but they also have other information, such as illumination and geometrical distortions due to the unconstrained conditions and the movement of the hand. In this case, the sparse representation assumption may not hold well in the original space since samples from different classes may be considered from the same class. This paper aims to enhance palmprint identification performance through SRC by proposing a simple yet efficient method based on an ensemble of sparse representations through an ensemble of discriminative dictionaries satisfying SRC assumption. The ensemble learning has the advantage to reduce the sensitivity due to the limited size of the training data and is performed based on random subspace sampling over 2D-PCA space while keeping the image inherent structure and information. In order to obtain discriminative dictionaries satisfying SRC assumption, a new space is learned by minimizing and maximizing the intra-class and inter-class variations using 2D-LDA. Extensive experiments are conducted on two publicly available palmprint data sets: multispectral and PolyU. Obtained results showed very promising results compared with both state-of-the-art holistic and coding methods. Besides these findings, we provide an empirical analysis of the parameters involved in the proposed technique to guide the neophyte.

Improved Model-Free Gait Recognition Based on Human Body Part

Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions, and angle variations that adversely affect the recognition performance. This chapter proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA gait database (dataset B), and the experimental results suggest that our method yields 88.75 % of Correct Classification Rate (CCR) when compared to existing state-of-the-art methods.

Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections

Gait recognition aims to identify people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations and carrying conditions that adversely affect the recognition performances. This paper proposes a novel method which combines Statistical Dependency (SD) feature selection with Globality-Locality Preserving Projections (GLPP) to alleviate the impact of intra-class variations so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait database (Dataset B) under variations of clothing and carrying conditions. The experimental results demonstrate that the proposed method achieves a Correct Classification Rate (CCR) up to 86% when compared to existing state-of-the-art methods.

Palmprint identification using sparse and dense hybrid representation

Among various palmprint identification methods proposed in the literature, Sparse Representation for Classification (SRC) is very attractive, offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In fact, palmprint images do not only contain identity information but they also have other information such as illumination and distortions due the acquisition conditions. In this case, SRC may not be able to classify the identity of palmprint well in the original space since samples from the same class show large variations. To overcome this problem, we propose in this work to exploit sparse-and-dense hybrid representation (SDR) for palmprint identification. Indeed, this type of representations that are based on the dictionary learning from the training data has shown its great advantage to overcome the limitations of SRC. Extensive experiments are conducted on two publicly available palmprint datasets: multispectral and PolyU. The obtained results clearly show the ability of the proposed method to outperform both the state-of-the-art holistic approaches and the coding palmprint identification methods.

Robust gait recognition: a comprehensive survey

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

Supervised Music Chord Recognition

Chord represents the back-bone of occidental music genre as it contains rich harmonic information which is useful for various music applications such as music genre classification or music retrieval. Hence, chord recognition or transcription is of importance for music representation. In this paper we focus on chord recognition and especially investigate different features representation used in such a system: classical features as well as a new type of feature we propose are explored. We evaluate their usefulness through a multi-class chord classification problem.