no code implementations • 21 Apr 2024 • Steven A. Grosz, Anil K. Jain
The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data.
no code implementations • 16 Jan 2024 • Steven A. Grosz, Akash Godbole, Anil K. Jain
Most prior work focuses on extracting global features or local features alone for palmprint matching, whereas this research introduces a novel framework that combines global and local features for enhanced palmprint matching accuracy.
no code implementations • 20 Nov 2023 • Sai Amrit Patnaik, Shivali Chansoriya, Anil K. Jain, Anoop M. Namboodiri
We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting.
no code implementations • 1 Jun 2023 • André Brasil Vieira Wyzykowski, Anil K. Jain
One innovative element of this approach includes a novel Fingerprint Enhancement Gabor layer, specifically designed for GPU computations.
no code implementations • 31 May 2023 • Andre Brasil Vieira Wyzykowski, Anil K. Jain
This study aims to develop a fast method, which we call ULPrint, to enhance various latent fingerprint types, including those obtained from real crime scenes and laboratory-created samples, to boost fingerprint recognition system performance.
no code implementations • 12 May 2023 • Steven A. Grosz, Kanishka P. Wijewardena, Anil K. Jain
In this work, we leverage a vision transformer architecture for joint spoof detection and matching and report competitive results with state-of-the-art (SOTA) models for both a sequential system (two ViT models operating independently) and a unified architecture (a single ViT model for both tasks).
no code implementations • 9 May 2023 • Akash Godbole, Steven A. Grosz, Anil K. Jain
Using a contactless child palmprint database, Child-PalmDB1, consisting of 19, 158 images from 1, 020 unique palms (in the age range of 6 mos.
no code implementations • 26 Apr 2023 • Steven A. Grosz, Anil K. Jain
One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i. e., latent prints or fingermarks).
no code implementations • 14 Dec 2022 • Anil K. Jain, Akash Godbole, Anjoo Bhatnagar, Prem Sewak Sudhish
Developing and least developed countries face the dire challenge of ensuring that each child in their country receives required doses of vaccination, adequate nutrition and proper medication.
no code implementations • 25 Nov 2022 • Steven A. Grosz, Anil K. Jain
combining the complimentary representations of attention-based and CNN-based embeddings for improved state-of-the-art (SOTA) fingerprint recognition (both authentication and identification).
no code implementations • 25 Oct 2022 • Steven A. Grosz, Joshua J. Engelsma, Rajeev Ranjan, Naveen Ramakrishnan, Manoj Aggarwal, Gerard G. Medioni, Anil K. Jain
We further demonstrate that by guiding the ViT to focus in on local, minutiae related features, we can boost the recognition performance.
no code implementations • 2 Sep 2022 • Akash Godbole, Karthik Nandakumar, Anil K. Jain
While learning an ensemble of representations can mitigate this problem, two critical challenges need to be addressed: (i) How to extract multiple diverse representations from the same fingerprint image?
no code implementations • 29 Aug 2022 • Andre Brasil Vieira Wyzykowski, Anil K. Jain
Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print.
no code implementations • 19 May 2022 • Akash Godbole, Steven A. Grosz, Karthik Nandakumar, Anil K. Jain
Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems.
no code implementations • 8 May 2022 • Kanishka P. Wijewardena, Steven A. Grosz, Kai Cao, Anil K. Jain
We show that while a deep template can be inverted to produce a fingerprint image that could be matched to its source image, deep templates are more resistant to reconstruction attacks than minutiae templates.
no code implementations • 13 Apr 2022 • Steven A. Grosz, Anil K. Jain
This work aims to demonstrate the utility of synthetic (both live and spoof) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint spoof detection algorithms beyond the capabilities when training on a limited amount of publicly available real datasets.
7 code implementations • CVPR 2022 • Minchul Kim, Anil K. Jain, Xiaoming Liu
In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality.
Ranked #1 on Surveillance-to-Booking on IJB-S
1 code implementation • 13 Jan 2022 • Shaoxiong Zhang, Yunhong Wang, Tianrui Chai, Annan Li, Anil K. Jain
Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait.
no code implementations • 10 Jan 2022 • Joshua J. Engelsma, Steven A. Grosz, Anil K. Jain
The publicly available datasets that do exist contain very few identities and impressions per finger.
no code implementations • 12 Jul 2021 • Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
no code implementations • 14 May 2021 • Anil K. Jain, Debayan Deb, Joshua J. Engelsma
Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe.
no code implementations • 7 Apr 2021 • Divyansh Aggarwal, Jiayu Zhou, Anil K. Jain
DNN-based face recognition models require large centrally aggregated face datasets for training.
no code implementations • 6 Apr 2021 • Steven A. Grosz, Joshua J. Engelsma, Eryun Liu, Anil K. Jain
Matching contactless fingerprints or finger photos to contact-based fingerprint impressions has received increased attention in the wake of COVID-19 due to the superior hygiene of the contactless acquisition and the widespread availability of low cost mobile phones capable of capturing photos of fingerprints with sufficient resolution for verification purposes.
no code implementations • 5 Apr 2021 • Debayan Deb, Xiaoming Liu, Anil K. Jain
Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94. 73% @ 0. 2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories.
no code implementations • 28 Nov 2020 • Debayan Deb, Xiaoming Liu, Anil K. Jain
During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces and a purifier attempts to remove the adversarial perturbations in the image space.
1 code implementation • CVPR 2021 • Yichun Shi, Divyansh Aggarwal, Anil K. Jain
We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation.
2 code implementations • 13 Oct 2020 • Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang
However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.
1 code implementation • 7 Oct 2020 • Joshua J. Engelsma, Debayan Deb, Kai Cao, Anjoo Bhatnagar, Prem S. Sudhish, Anil K. Jain
In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition.
no code implementations • 16 Sep 2020 • Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain, Jiayu Zhou
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
no code implementations • 1 Aug 2020 • Steven A. Grosz, Joshua J. Engelsma, Anil K. Jain
While a few studies have conducted white-box evaluations of the fingerprint reader, feature extractor, and matching components, no existing study has provided a full system, white-box analysis of the uncertainty introduced at each stage of a fingerprint recognition system.
no code implementations • CVPR 2021 • Sixue Gong, Xiaoming Liu, Anil K. Jain
Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes.
no code implementations • 4 Jun 2020 • Debayan Deb, Anil K. Jain
State-of-the-art spoof detection methods tend to overfit to the spoof types seen during training and fail to generalize to unknown spoof types.
no code implementations • 8 Apr 2020 • Cori Tymoszek, Sunpreet S. Arora, Kim Wagner, Anil K. Jain
We present our ongoing work on developing a system, called DashCam Pay, that enables in-vehicle payments in a seamless and secure manner using face and voice biometrics.
no code implementations • 6 Apr 2020 • Steven A. Grosz, Tarang Chugh, Anil K. Jain
The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i. e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods.
1 code implementation • 27 Mar 2020 • Joshua J. Engelsma, Anil K. Jain, Vishnu Naresh Boddeti
We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain.
no code implementations • 17 Mar 2020 • Debayan Deb, Divyansh Aggarwal, Anil K. Jain
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age.
no code implementations • 17 Mar 2020 • Yichun Shi, Anil K. Jain
In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets.
no code implementations • CVPR 2020 • Yichun Shi, Xiang Yu, Kihyuk Sohn, Manmohan Chandraker, Anil K. Jain
Recognizing wild faces is extremely hard as they appear with all kinds of variations.
no code implementations • 17 Dec 2019 • Tarang Chugh, Anil K. Jain
We utilize the dynamics involved in the imaging of a fingerprint on a touch-based fingerprint reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers.
no code implementations • 16 Dec 2019 • Vishesh Mistry, Joshua J. Engelsma, Anil K. Jain
Evaluation of large-scale fingerprint search algorithms has been limited due to lack of publicly available datasets.
no code implementations • 8 Dec 2019 • Rohit Gajawada, Additya Popli, Tarang Chugh, Anoop Namboodiri, Anil K. Jain
Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones.
no code implementations • 5 Dec 2019 • Tarang Chugh, Anil K. Jain
The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75. 24% to 91. 78% @ FDR = 0. 2%.
1 code implementation • ECCV 2020 • Sixue Gong, Xiaoming Liu, Anil K. Jain
We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups.
no code implementations • 18 Nov 2019 • Debayan Deb, Divyansh Aggarwal, Anil K. Jain
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age.
no code implementations • 21 Sep 2019 • Joshua J. Engelsma, Kai Cao, Anil K. Jain
DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation.
4 code implementations • 17 Sep 2019 • Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
no code implementations • 2 Sep 2019 • Steven A. Grosz, Joshua J. Engelsma, Nicholas G. Paulter Jr., Anil K. Jain
While a few studies have conducted stand-alone evaluations of the fingerprint reader and feature extraction modules of fingerprint recognition systems, little work has been devoted towards white-box evaluations of the fingerprint matching module.
1 code implementation • 14 Aug 2019 • Debayan Deb, Jianbang Zhang, Anil K. Jain
Face recognition systems have been shown to be vulnerable to adversarial examples resulting from adding small perturbations to probe images.
no code implementations • 31 Jul 2019 • Tarang Chugh, Anil K. Jain
Optical coherent tomography (OCT) fingerprint technology provides rich depth information, including internal fingerprint (papillary junction) and sweat (eccrine) glands, in addition to imaging any fake layers (presentation attacks) placed over finger skin.
no code implementations • 27 May 2019 • Dinh-Luan Nguyen, Anil K. Jain
Because pores are one of the most reliable features besides minutiae to identify latent fingerprints, we propose an end-to-end automatic pore extraction and matching system to analyze the utility of pores in latent fingerprint identification.
no code implementations • 26 Apr 2019 • Sixue Gong, Yichun Shi, Anil K. Jain
Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis.
no code implementations • 25 Apr 2019 • Nathan D. Kalka Noblis, Brianna Maze; James A. Duncan, Kevin O’Connor, Stephen Elliott, Kaleb Hebert, Julia Bryan, Anil K. Jain
We present IJB-S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols.
1 code implementation • ICCV 2019 • Yichun Shi, Anil K. Jain
Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space.
Ranked #1 on Face Verification on IJB-C (training dataset metric)
1 code implementation • 1 Apr 2019 • Joshua J. Engelsma, Debayan Deb, Anil K. Jain, Prem S. Sudhish, Anjoo Bhatnager
In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition.
no code implementations • 1 Apr 2019 • Joshua J. Engelsma, Kai Cao, Anil K. Jain
We learn a discriminative fixed length feature representation of fingerprints which stands in contrast to commonly used unordered, variable length sets of minutiae points.
no code implementations • 19 Feb 2019 • Sixue Gong, Yichun Shi, Anil K. Jain
We propose a new approach to video face recognition.
no code implementations • 16 Jan 2019 • Debayan Deb, Arun Ross, Anil K. Jain, Kwaku Prakah-Asante, K. Venkatesh Prasad
Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris.
no code implementations • 13 Jan 2019 • Joshua J. Engelsma, Anil K. Jain
Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
no code implementations • 10 Jan 2019 • Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
no code implementations • 30 Dec 2018 • Tarang Chugh, Anil K. Jain
We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training.
3 code implementations • CVPR 2019 • Yichun Shi, Debayan Deb, Anil K. Jain
We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo.
no code implementations • 1 Nov 2018 • Hu Han, Jie Li, Anil K. Jain, Shiguang Shan, Xilin Chen
To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning.
1 code implementation • 15 Sep 2018 • Yichun Shi, Anil K. Jain
Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators.
1 code implementation • 6 May 2018 • Yichun Shi, Anil K. Jain
Numerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e. g. passports and driver licenses.
no code implementations • 2 May 2018 • Elham Tabassi, Tarang Chugh, Debayan Deb, Anil K. Jain
Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS.
2 code implementations • 27 Apr 2018 • Kai Cao, Anil K. Jain
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.
3 code implementations • 25 Apr 2018 • Dinh-Luan Nguyen, Kai Cao, Anil K. Jain
We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet.
no code implementations • 23 Apr 2018 • Joshua J. Engelsma, Kai Cao, Anil K. Jain
We open source fingerprint Match in Box, a complete end-to-end fingerprint recognition system embedded within a 4 inch cube.
no code implementations • 22 Apr 2018 • Debayan Deb, Tarang Chugh, Joshua Engelsma, Kai Cao, Neeta Nain, Jake Kendall, Anil K. Jain
We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images.
2 code implementations • CVPR 2019 • Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain
This paper addresses the following questions pertaining to the intrinsic dimensionality of any given image representation: (i) estimate its intrinsic dimensionality, (ii) develop a deep neural network based non-linear mapping, dubbed DeepMDS, that transforms the ambient representation to the minimal intrinsic space, and (iii) validate the veracity of the mapping through image matching in the intrinsic space.
3 code implementations • 26 Dec 2017 • Dinh-Luan Nguyen, Kai Cao, Anil K. Jain
Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets.
1 code implementation • 26 Dec 2017 • Joshua J. Engelsma, Kai Cao, Anil K. Jain
Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.
no code implementations • 12 Dec 2017 • Tarang Chugh, Kai Cao, Anil K. Jain
Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios.
1 code implementation • CVPR 2018 • Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
1 code implementation • 10 Nov 2017 • Debayan Deb, Neeta Nain, Anil K. Jain
Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers.
no code implementations • 29 Sep 2017 • Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain
Numerical experiments on unconstrained faces (IJB-C) provides a capacity upper bound of $2. 7\times10^4$ for FaceNet and $8. 4\times10^4$ for SphereFace representation at a false acceptance rate (FAR) of 1%.
no code implementations • 25 Aug 2017 • Joshua J. Engelsma, Kai Cao, Anil K. Jain
Finally, fingerprint matching experiments between images acquired from the FTIR output of the RaspiReader and images acquired from a COTS fingerprint reader verify the interoperability of the RaspiReader with existing COTS optical readers.
1 code implementation • KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 • Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #4 on Multivariate Time Series Forecasting on USHCN-Daily
no code implementations • 29 Jun 2017 • Lacey Best-Rowden, Anil K. Jain
Face image quality can be defined as a measure of the utility of a face image to automatic face recognition.
no code implementations • 15 Jun 2017 • Yichun Shi, Charles Otto, Anil K. Jain
Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarity between face images.
no code implementations • 3 Jun 2017 • Hu Han, Anil K. Jain, Fang Wang, Shiguang Shan, Xilin Chen
In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes.
Ranked #5 on Facial Attribute Classification on LFWA
no code implementations • 22 May 2017 • Joshua J. Engelsma, Sunpreet S. Arora, Anil K. Jain, Nicholas G. Paulter Jr
We present the design and manufacturing of high fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical.
2 code implementations • 6 Apr 2017 • Kai Cao, Anil K. Jain
Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64. 7% for the NIST SD27 and 75. 3% for the WVU latent databases, against a reference database of 100K rolled prints.
no code implementations • 2 Mar 2017 • Guangcan Mai, Kai Cao, Pong C. Yuen, Anil K. Jain
Our study demonstrates the need to secure deep templates in face recognition systems.
1 code implementation • 30 Sep 2016 • Inci M. Baytas, Ming Yan, Anil K. Jain, Jiayu Zhou
The models for each hospital may be different because of the inherent differences in the distributions of the patient populations.
1 code implementation • 4 Apr 2016 • Charles Otto, Dayong Wang, Anil K. Jain
Additionally, we present preliminary work on video frame clustering (achieving 0. 71 F-measure when clustering all frames in the benchmark YouTube Faces dataset).
no code implementations • 16 Nov 2015 • Siyuan Huang, Jiwen Lu, Jie zhou, Anil K. Jain
In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets.
no code implementations • 26 Jul 2015 • Dayong Wang, Charles Otto, Anil K. Jain
We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images.
Ranked #15 on Face Verification on IJB-A
no code implementations • CVPR 2015 • Brendan F. Klare, Ben Klein, Emma Taborsky, Austin Blanton, Jordan Cheney, Kristen Allen, Patrick Grother, Alan Mah, Anil K. Jain
Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations.
no code implementations • 17 Apr 2015 • Anil K. Jain, Sunpreet S. Arora, Lacey Best-Rowden, Kai Cao, Prem Sewak Sudhish, Anjoo Bhatnagar
However, persistence of biometric recognition accuracy has not been studied systematically for children in the age group of 0-4 years.
1 code implementation • 6 Aug 2014 • Shengcai Liao, Anil K. Jain, Stan Z. Li
First, a new image feature called Normalized Pixel Difference (NPD) is proposed.
Ranked #6 on Face Detection on PASCAL Face
no code implementations • 16 Feb 2014 • Radha Chitta, Rong Jin, Timothy C. Havens, Anil K. Jain
Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data.
no code implementations • NeurIPS 2012 • Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain
One difficulty in learning the pairwise similarity measure is that there is a significant amount of noise and inter-worker variations in the manual annotations obtained via crowdsourcing.
no code implementations • NeurIPS 2010 • Serhat Bucak, Rong Jin, Anil K. Jain
Recent studies have shown that multiple kernel learning is very effective for object recognition, leading to the popularity of kernel learning in computer vision problems.