Recognition of blurred faces using Local Phase Quantization

By Timo Ahonen, Esa Rahtu, Ville Ojansivu, Janne Heikkilä. International Conference on Pattern Recognition, 2008

336 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/934d827c0f423e318cff3bb2ed110d68e00569c8

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Frankenstein: Learning Deep Face Representations Using Small Data (2016, neutral): “Such linear features were later replaced by hand-crafted local non-linear features, such as Local Binary Patterns [1], Local Phase Quantisation (LPQ) [2], and Fisher vectors computed over dense SIFT descriptors [43].”
Past, Present, and Future of Face Recognition: A Review (2020, methodology): “[91] (2008) used the newly introduced operator: local phase quantization (LPQ) [92] in recognition of blurred faces to solve the facial recognition issue under blurring situations.”
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation (2013, methodology): “Besides, we also compare with the Local Phase Quantization (LPQ) [2] method, which is a blur insensitive descriptor, and the method in [5], which estimates an albedo map (Albedo) as an illumination robust signature for matching.”
BSIF: Binarized statistical image features (2012, methodology): “We use standard 8 bit coding for LBP and LPQ, which results in a feature vector with 256 elements.”
Face Recognition Across Non-Uniform Motion Blur, Illumination, and Pose (2015, methodology): “a nearest neighbour classifier using Chi square distance [16] with the obtained histograms as feature vectors.”
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition (2016, methodology): “[31] proposed to employ the blur-insensitive Local Phase Quantization (LPQ) descriptor for facial feature extraction, which has been widely used in VFR applications [9].”
When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition (2015, background): “In constrained environments, hand-crafted features such as Local Binary Patterns (LBP) [1] and Local Phase Quantisation (LPQ) [2, 4] have achieved respectable face recognition performance.”
Learning Discriminant Face Descriptor (2014, background): “Besides LBP and its variants (TPLBP, FPLBP [33]), LPQ [52], SIFT [53], and HOG [54], 2 which are popular descriptors in heterogeneous face recognition [55], [56], are also compared.”
Robust Face Recognition via Multimodal Deep Face Representation (2015, background): “Conventional face representations are built on local descriptors, e . g ., Local Binary Patterns (LBP) [3], Local Phase Quantization (LPQ) [4], [5], Dual-Cross Patterns (DCP) [6], and Binarised Statistical Image Features (BSIF) [7].”
A Blur-Robust Descriptor with Applications to Face Recognition (2012, background): “There have been extensions of these works which, in addition to blur, accommodate invariance to rotation, similarity, and affine transformations [10], [30], and have been used for recognizing objects/faces in distorted images [3], [24].”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “Index Terms—Face recognition, face image descriptors, face image representation F”