82,466 research outputs found

    Face recognition using statistical adapted local binary patterns.

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    Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images

    Design and implementation of a multi-modal biometric system for company access control

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    This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database

    Dynamic fast local Laplacian completed local ternary pattern (dynamic FLapCLTP) for face recognition

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    Today, face recognition has become one of the typical biometric authentication systems used for high security. Some systems may use face recognition to enhance their security and provide high protection level. Feature extraction is considered to be one of the most important steps in face recognition systems. The important and interesting parts of the image in feature extraction are represented as a compact feature vector. Many features, such as texture, colour and shape, have been proposed in the image processing fields. These features can also be classified globally or locally depending on the image extraction area. Texture descriptors have recently played a crucial role as local descriptors. Different types of texture descriptors, such as local binary pattern (LBP), local ternary pattern (LTP), completed local binary pattern (CLBP) and completed local ternary pattern (CLTP), have been proposed and utilised for face recognition tasks. All these texture features have achieved good performance in terms of recognition accuracy. Although the LBP performed well in different tasks, it has two limitations. LBP is sensitive to noise and occasionally fails to clearly distinguish between two different texture patterns with the same LBP encoding code. Most of the texture descriptors inherited these limitations from LBP. CLTP is proposed to overcome the limitations of LBP. CLTP performed well with different image processing tasks, such as image classification and face recognition. However, CLTP suffers from two limitations that may affect its performance in these tasks: the fixed value of the threshold value that is used during the CLTP extraction process regardless of the type of dataset or system and the longer length of the CLTP histogram than that of previous descriptors. This study focused on handling the first limitation, which is the threshold selection. Firstly, a new texture descriptor is proposed by integrating the fast-local Laplacian filter and the CLTP descriptor, namely, fast-local Laplacian CLTP (FLapCLTP). The fast-local Laplacian filter can help in increasing the performance of the CLTP due to its extensive detail enhancements and tone mapping; this contribution is handled by the constant threshold value used in CLTP. A dynamic FLapCLTP is then proposed to address the aforementioned issue. Instead of using a fixed threshold value with all datasets, a dynamic value is selected based on the image pixel values. Therefore, each different texture pattern has different threshold values to extract FLapCLTP from the pattern. This dynamic value is automatically selected according to the centre value of the texture pattern. Therefore, a dynamic FLapCLTP is proposed in this study. Finally, the proposed FLapCLTP and dynamic FLapCLTP are evaluated for facial recognition systems using ORL Faces, Sheffield Face, Collection Facial Images, Georgia Tech Face, Caltech Pedestrian Faces 1999, JAFFE, FEI Face and YALE datasets. The results showed the priority of the proposed texture compared with previous texture descriptors. The dynamic FLapCLTP achieved the highest recognition accuracy rates with values of 100%, 99.96%, 99.75%, 99.69%, 94.86%, 90.33%, 86.86% and 82.43% using UMIST, Collection Facial Images, JAFFE, ORL, Georgia Tech, YALE, Caltech 1999 and FEI datasets, respectively

    Sparse Radial Sampling LBP for Writer Identification

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    In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis and Recognition (ICDAR 2015

    Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval

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    Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than 90%90\% under 50%50\% impulse noise densities, more than 95%95\% under Gaussian noised textures with standard deviation σ=5\sigma = 5, and more than 99%99\% under Gaussian blurred textures with standard deviation σ=1.25\sigma = 1.25. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise
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