Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. (2005)

by W G Zhang, S G Shan, W Gao, H M Zhang
Venue:Proceedings of IEEE International Conference on Vision,
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Face description with local binary patterns: Application to face recognition

by Abdenour Hadid, Senior Member - IEEE Trans. Pattern Analysis and Machine Intelligence , 2006
"... Abstract—This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a ..."
Abstract - Cited by 526 (27 self) - Add to MetaCart
Abstract—This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed. Index Terms—Facial image representation, local binary pattern, component-based face recognition, texture features, face misalignment. Ç 1
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Enhanced local texture feature sets for face recognition under difficult lighting conditions

by Xiaoyang Tan, Bill Triggs - In Proc. AMFG’07 , 2007
"... 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. S ..."
Abstract - Cited by 274 (10 self) - Add to MetaCart
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
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A Comparative Study of Local Matching Approach for Face Recognition

by Jie Zou, Qiang Ji, George Nagy , 2007
"... In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The c ..."
Abstract - Cited by 61 (1 self) - Add to MetaCart
In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The conclusions from our experiments include: (1) additional evidence that Gabor features are effective local feature representations and are robust to illumination changes; (2) discrimination based only on a small portion of the face area is surprisingly good; (3) the configuration of facial components does contain rich discriminating information and comparing corresponding local regions utilizes shape features more effectively than comparing corresponding facial components; (4) spatial multiresolution analysis leads to better classification performance; (5) combining local regions with Borda Count classifier combination method alleviates the curse of dimensionality. We implemented a complete face recognition system by integrating the best option of each step. Without training, illumination compensation and without any parameter tuning, it achieves superior performance on every category of the FERET test: near perfect classification accuracy (99.5%) on pictures taken on the same day regardless of indoor illumination variations; and significantly better than any other reported performance on pictures taken several days to more than a year apart. The most significant experiments were repeated on the AR database, with similar results.
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Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary

by Meng Yang, Lei Zhang - In ECCV 2010
"... Abstract. By coding the input testing image as a sparse linear combination of the training samples via l1-norm minimization, sparse representation based classification (SRC) has been recently successfully used for face recognition (FR). Particularly, by introducing an identity occlusion dictionary t ..."
Abstract - Cited by 59 (13 self) - Add to MetaCart
Abstract. By coding the input testing image as a sparse linear combination of the training samples via l1-norm minimization, sparse representation based classification (SRC) has been recently successfully used for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to sparsely code the occluded portions in face images, SRC can lead to robust FR results against occlusion. However, the large amount of atoms in the occlusion dictionary makes the sparse coding computationally very expensive. In this paper, the image Gabor-features are used for SRC. The use of Gabor kernels makes the occlusion dictionary compressible, and a Gabor occlusion dictionary computing algorithm is then presented. The number of atoms is significantly reduced in the computed Gabor occlusion dictionary, which greatly reduces the computational cost in coding the occluded face images while improving greatly the SRC accuracy. Experiments on representative face databases with variations of lighting, expression, pose and occlusion demonstrated the effectiveness of the proposed Gabor-feature based SRC (GSRC) scheme. 1
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Hierarchical ensemble of global and local classifiers for face recognition

by Yu Su, Shiguang Shan, Xilin Chen, Wen Gao - in Proc. IEEE Int. Conf. Computer Vision
"... Abstract—In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this ..."
Abstract - Cited by 58 (6 self) - Add to MetaCart
Abstract—In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher’s linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.
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WLD: A robust local image descriptor

by Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen, Senior Member, Xilin Chen, Senior Member, Wen Gao - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2010
"... Abstract—Inspired by Weber’s Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the or ..."
Abstract - Cited by 51 (1 self) - Add to MetaCart
Abstract—Inspired by Weber’s Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set. Index Terms—Pattern recognition, Weber law, local descriptor, texture, face detection. Ç 1
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Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

by Xiaoyang Tan, Bill Triggs
"... Abstract. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition ..."
Abstract - Cited by 50 (0 self) - Add to MetaCart
Abstract. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results. 1
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Face Recognition using Local Quantized Patterns

by Sibt Ul Hussain, Thibault Napoléon, Fréderic Jurie , 2012
"... This paper proposes a novel face representation based on Local Quantized Patterns (LQP). LQP is a generalization of local pattern features that makes use of vector quantization and lookup table to let local pattern features have many more pixels and/or quantization levels without sacrificing simplic ..."
Abstract - Cited by 31 (2 self) - Add to MetaCart
This paper proposes a novel face representation based on Local Quantized Patterns (LQP). LQP is a generalization of local pattern features that makes use of vector quantization and lookup table to let local pattern features have many more pixels and/or quantization levels without sacrificing simplicity and computational efficiency. Our new LQP face representation not only outperforms any other representation on challenging face datasets but performs equally well in the intensity space and orientation space (obtained by applying gradient or Gabor Filters) and hence is intrinsically robust to illumination variations. Extensive experiments on several challenging face recognition datasets (such as FERET and LFW) show that this representation gives state-of-the-art performance (improving the earlier state-of-the-art by around 3%) without requiring neither a metric learning stage nor a costly labelled training dataset, having the comparison of two faces being made by simply computing the Cosine similarity between their LQP representations in a projected space.

Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition

by Shufu Xie, Shiguang Shan, Xilin Chen, Jie Chen - IEEE TRANS. IMAGE PROCESSING , 2010
"... Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, ..."
Abstract - Cited by 30 (1 self) - Add to MetaCart
Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we first propose local Gabor XOR patterns (LGXP), which encodes the Gabor phase by using the local XOR pattern (LXP) operator. Then, we introduce block-based Fisher’s linear discriminant (BFLD) to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power. Finally, by using BFLD, we fuse local patterns of Gabor magnitude and phase for face recognition. We evaluate our approach on FERET and FRGC 2.0 databases. In particular, we perform comparative experimental studies of different local Gabor patterns. We also make a detailed comparison of their combinations with BFLD, as well as the fusion of different descriptors by using BFLD. Extensive experimental results verify the effectiveness of our LGXP descriptor and also show that our fusion approach outperforms most of the state-of-the-art approaches.
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A Robust and Scalable Approach to Face Identification

by William Robson Schwartz, Huimin Guo, Larry S. Davis
"... Abstract. The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of condition ..."
Abstract - Cited by 30 (12 self) - Add to MetaCart
Abstract. The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of conditions, techniques that are both robust to uncontrolled acquisition conditions and scalable to large gallery sizes, which may need to be incrementally built, are challenges. In this work we tackle both problems. Initially, we propose a novel approach to robust face identification based on Partial Least Squares (PLS) to perform multi-channel feature weighting. Then, we extend the method to a tree-based discriminative structure aiming at reducing the time required to evaluate novel probe samples. The method is evaluated through experiments on FERET and FRGC datasets. In most of the comparisons our method outperforms stateof-art face identification techniques. Furthermore, our method presents scalability to large datasets.
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