This paper presents a computationally efficient yet powerful binary framework for robust facial representation based on image gradients. It is termed as structural binary gradient patterns (SBGP). To discover underlying local structures in the gradient domain, we compute image gradients from multiple directions and simplify them into a set of binary strings. The SBGP is derived from certain types of these binary strings that have meaningful local structures and are capable of resembling fundamental textural information. They detect micro orientational edges and possess strong orientation and locality capabilities, thus enabling great discrimination. The SBGP also benefits from the advantages of the gradient domain and exhibits profound robustness against illumination variations. The binary strategy realized by pixel correlations in a small neighborhood substantially simplifies the computational complexity and achieves extremely efficient processing with only 0.0032s in Matlab for a typical face image. Furthermore, the discrimination power of the SBGP can be enhanced on a set of defined orientational image gradient magnitudes, further enforcing locality and orientation. Results of extensive experiments on various benchmark databases illustrate significant improvements of the SBGP based representations over the existing state-of-the-art local descriptors in the terms of discrimination, robustness and complexity. Codes for the SBGP methods will be available at this http URL
As a simpler approach, Jie @cite_11 proposed a Weber local descriptor (WLD) based on the Weber's Law of human perception system, which states that the noticeable change of a stimulus is a constant ratio of the original stimulus. In @cite_62 , Tan and Triggs presented local ternary patterns (LTP) by extending LBP to 3-valued codes for increasing its robustness to noise in the near-uniform image regions. Both methods have been shown to be highly discriminative and resistant to illumination changes, extending the advantages of LBP. However, similar to LBP, both descriptors build local relationships in the intensity domain, which can be seriously affected by dramatic changes of pixel intensity.
@cite_62 @cite_11
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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. Features such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) have been very successful in a number of areas including texture analysis, face recognition and object detection. They are based on the idea that small patterns of qualitative local gray-level differences contain a great deal of information about higher-level image content. Current local pattern features use hand-specified codings that are limited to small spatial supports and coarse graylevel comparisons. We introduce Local Quantized Patterns (LQP), a generalization that uses lookup-table-based vector quantization to code larger or deeper patterns. LQP inherits some of the flexibility and power of visual word representations without sacrificing the run-time speed and simplicity of local pattern ones. We show that it outperforms well-established features including HOG, LBP and LTP and their combinations on a range of challenging object detection and texture classification problems. Local binary pattern (LBP) and its variants are effective descriptors for face recognition. The traditional LBP like features are extracted based on the original pixel or patch values of images. In this paper, we propose to learn the discriminative image filter to improve the discriminant power of the LBP like feature. The basic idea is after the image filtering with the learned filter, the difference of pixel difference vectors (PDVs) between the images from the same person is consistent and the difference between the images from different persons is enlarged. In this way, the LBP like features extracted from the filtered images are considered to be more discriminant than those extracted from the original images. Moreover, a coupled discriminant image filters learning method is proposed to deal with the heterogenous face images matching problem by reducing the feature gap between the heterogeneous images. Experiments on FERET, FRGC and a VIS-NIR heterogeneous face databases validate the effectiveness of our proposed image filter learning method combined with LBP like features. This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal information of each pixel and the overall ordinal information is used to divide the local patch into subregions which are used for accumulating the LIOPs respectively. Therefore, both local and overall intensity ordinal information of the local patch are captured by the proposed LIOP descriptor so as to make it a highly discriminative descriptor. It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. The proposed descriptor has been evaluated on the standard Oxford dataset and four additional image pairs with complex illumination changes. The experimental results show that the proposed descriptor obtains a significant improvement over the existing state-of-the-art descriptors.