50 years of biometric research: Accomplishments, challenges, and opportunities

By Anil K. Jain, Karthik Nandakumar, Arun A. Ross. 2016

3,307 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “A simple domain-agnostic filtering approach for feature selection is based on information theory and statistics (Pudil et al., 1994; Yang and Pedersen, 1997; Dash and Liu, 1997; Guyon and Elisseeff, 2003).”
Survey on Automated Machine Learning (2019, background): “By examining the features from an information theoretic or statistics viewpoint (Pudil et al., 1994; Yang and Pedersen, 1997; Dash and Liu, 1997; Guyon and Elisseeff, 2003), no domain knowledge is necessary for feature selection.”
Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis (2015, background): “Floating techniques [32] allow backtracking for an arbitrary number of times as long as the quality criterion J is improving.”
A Robust-Equitable Measure for Feature Ranking and Selection (2017, methodology): “, sequential forward/backward search (Pudil et al., 1994)).”
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks (2020, methodology): “ing selection (SFFS) mechanism [29] has been applied on Algorithm 2 Sequential Forward Floating Selection”
Partially Supervised Graph Embedding for Positive Unlabelled Feature Selection (2016, background): “Sundararajan and Keerthi, 2011] and apuCFS [Borja Calvo and A.Lozano, 2009].”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “They include recursive feature elimination (22,40,99), floating search (100), and genetic algorithm (101).”
Social Media Text Classification under Negative Covariate Shift (2015, background): “One obvious solution to this problem is one-class classification (Schölkopf et al. 1999; Tax and Duin, 1999a), i.e., one-class SVM.”
A survey on feature selection methods (2014, methodology): “The SFS and SFFS methods suffer from producing nested subsets since the forward inclusion was always unconditional which means that two highly correlated variables might be included if it gave the highest performance in the SFS evaluation.”
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification (2009, methodology): “In both classiﬁcation steps feature selection was carried out by ‘Sequential Forward Floating Selection’ (SFFS) (Pudil et al., 1994) to establish the most discriminative subset of all calculated features.”
Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, methodology): “(Pudil et al. 1994), i.e., l = 2, r = 1; l = 3, r = 1; l = 3, r = 2.”
Automatic Classification for Pathological Prostate Images Based on Fractal Analysis (2009, methodology): “The sequential floating forward selection (SFFS) method is very effective in selecting an optimal subset of features [23].”
Feature selection for text classification: A review (2018, methodology): “Hence, the wrapper approach is only feasible when the number of features is relatively small [24, 71, 89].”
Using decision fusion of feature selection in digital forensics for camera source model identification (2012, methodology): “According to [33,35], we implemented five feature selection algorithms in Java: SFFS, SBFS, plus-2-minus-1 (P2M1), plus-3-minus-2 (P3M2), and plus-4-minus-3(P4M3).”
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware (2019, neutral): “, 2019; de Jesús Rubio, 2009), wrapper methods (Kohavi & John, 1997; Pudil et al., 1994; Reunanen, 2003; Eshelman, 1991; Cordón et al., 2006; Meda-Campaña, 2018; de Jesús Rubio, 2017) and embedded methods (Langley, 1994; Mundra & Rajapakse, 2009; Romero & Sopena, 2008).”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “Sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS) [25] were developed to overcome these problems by providing mechanisms to re-select the deleted features and delete the already added features.”