IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007 1 Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Citations in the corpus

• Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “The LBP-TOP operator calculates LBP features at three orthogonal planes that intersect Table 1 Employed test databases.”
• Recent advances and trends in visual tracking: A review (2011, background): “Zhao and Pietik¨ainen [54] proposed the dynamical Local Binary Patterns (DLBP) on three orthogonal planes, and used it for dynamic texture recognition.”
• DynTex: A comprehensive database of dynamic textures (2010, background): “…the aim is to recognize the type of dynamic texture, possibly from among several other ones (Péteri and ll rights reserved. ri), sandor.fazekas@sztaki.hu Chetverikov, 2005; Zhao and Pietikainen, 2007); a review on DT description and recognition is presented in (Chetverikov and Péteri, 2005).”
• Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval (2012, background): “The LBP operator on facial expression analysis and recognition is successfully reported in [22] and [23].”
• Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis (2016, methodology): “We also list the results from using only the 7-frame windows containing the AUs on both our approach (referred to as LN(7)) and LBP-TOP [36] (referred to as LBP(7)).”
• Violent flows: Real-time detection of violent crowd behavior (2012, background): “Recently, Local Binary Patterns (LBP), originally proposed for face and texture recognition in 2D images [25] and extended for 3D videos, have proven both effective and efficient in recognizing motion patterns [13, 33].”
• A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016, methodology): “We evaluate our proposed method on three spontaneous micro-expression databases (SMIC, CASME, and CASME II) and compare MDMO with two state-of-the-art baseline features (HOOF [22] and LBP-TOP [13], [23]).”
• Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns (2010, unknown): “The second approach in [46] com-”
• Discriminative local binary patterns for human detection in personal album (2008, background): “in [11] for the aim of texture classification, and then extended for various fields, including face recognition ([2]), face detection ([10]), facial expression recognition [19] etc.”
• Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods (2015, methodology): “ion length is discussed in Section V.B.1. D. Feature extraction Several spatial-temporal local texture descriptors (SLTD) have been demonstrated to be effective in tackling the FE recognition problem [47], [48]. Here we employ three kinds of SLTDs in our ME recognition framework to compare their per”
• Training deep networks for facial expression recognition with crowd-sourced label distribution (2016, background): “The cost of tagging a holistic facial emotion is often on the order of 1-2 US cents, which is orders of magnitude cheaper than FACS coding.”
• The first facial expression recognition and analysis challenge (2011, unknown): “[5], [20]), others only employ machine learning techniques to model time (e.”
• Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, background): “Among them, Gabor wavelet representation [3], [16], [37], [41], Local Binary Patterns (LBP) [42] and its variants such as Local Phase Quantization (LPQ) [12], [43] are two representative appearance-based feature extraction methods.”
• Extended local binary patterns for texture classification (2012, unknown): “Due to its impressive computational efficiency and good texture discriminative property, the dense LBP descriptor [4] has gained considerable attention since its publication [13], and has already been used in many other applications, including visual inspection, image retrieval, dynamic texture reco”
• Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015 (2015, methodology): “The parameters are: range = [0-360], pyramid levels = 3 and number of bins = 16.”
• Automatic facial expression recognition using features of salient facial patches (2015, background): “—————————— ——————————”