Face Authentication Using Adapted Local Binary Pattern Histograms

Rodriguez, Yann; Marcel, Sébastien

doi:10.1007/11744085_25

Cited by 69 publications

(60 citation statements)

References 23 publications

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“…Some recent developments in medical imaging (Liu et al, 2011), moving object detection (Trefny and Matas, 2010) and facial expression recognition (Ahmed et al, 2011) prove that the LBP texture model is still receiving a lot of attention. However LBP texture model is considered to be sensitive to noise especially in uniform regions (Rodriguez and Marcel, 2006). Moreover, it supports only a binary level comparison for encoding and thereby it is inadequate to represent the local texture information in more detail.…”

Section: Local Binary Patterns (Lbp)mentioning

confidence: 99%

Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Raja¹

2013

Journal of Computer Science

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Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in feature vector generation and comparison. As textures are oriented and scaled differently, a texture model should effectively handle grey-scale variation, rotation variation, illumination variation and noise. The length of the feature vector in a texture model also plays an important role in deciding the time complexity of the texture analysis. This study proposes a new texture model, called Optimized Local Ternary Patterns (OLTP) in the spatial methods of texture analysis. The proposed texture model is based on Local Ternary Patterns (LTP), which in turn is based on LBP. A new concept called "Level of Optimality" to select the optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patterns to extract the textural information from the digital images and thereby reducing the length of the feature vector. This proposed model is robust to image rotation, grey-scale transformation, histogram equalization and noise. The results are compared with other widely used texture models by applying classification tests to variety of texture images from the standard Brodatz texture database. Experimental results prove that the proposed texture model is robust to grey-scale variation, image rotation, histogram equalization and noise. Experimental results also show that the proposed texture model improves the classification accuracy and the speed of the classification process. In all tested tasks, the proposed method outperforms the earlier methods.

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Section: Local Binary Patterns (Lbp)mentioning

confidence: 99%

Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Raja¹

2013

Journal of Computer Science

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“…LBP's are a computationally efficient nonparametric local image texture descriptor. They have been used with considerable success in a number of visual recognition tasks including face recognition [1,2,20]. LBP features are invariant to monotonic gray-level changes by design and thus are usually considered to require no image preprocessing before use 1 .…”

Section: Introductionmentioning

confidence: 99%

Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

Tan

Triggs

Lecture Notes in Computer Science

866

947

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Abstract. Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

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“…In the past, a large number of approaches that tackled the problem of face recognition were based on generic face recognition algorithms such as PCA/LDA/ICA subspace analysis [20], or local binary pattern histograms (LBP) [19] and its extensions. The discrete cosine transform (DCT) has been used as a feature extraction step in various studies on face recognition, where the proposed local appearance-based face recognition approach in [4] outperformed e.g.…”

Section: Introductionmentioning

confidence: 99%

SURF-Face: Face Recognition Under Viewpoint Consistency Constraints

Dreuw

Steingrube

Hanselmann

et al. 2009

Procedings of the British Machine Vision Conference 2009

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We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated.Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized.A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail.The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

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Face Authentication Using Adapted Local Binary Pattern Histograms

Cited by 69 publications

References 23 publications

Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

SURF-Face: Face Recognition Under Viewpoint Consistency Constraints

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