Improving cross-resolution face matching using ensemble-based co-transfer learning.
09 Oct 2014-IEEE Transactions on Image Processing (IEEE Trans Image Process)-Vol. 23, Iss: 12, pp 5654-5669
TL;DR: A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching and enhances the performance of cross- resolution face recognition.
Abstract: Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery. A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.
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TL;DR: Online learning as mentioned in this paper is a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time.
Abstract: Online learning represents a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time. The goal of online learning is to maximize the accuracy/correctness for the sequence of predictions/decisions made by the online learner given the knowledge of correct answers to previous prediction/learning tasks and possibly additional information. This is in contrast to traditional batch or offline machine learning methods that are often designed to learn a model from the entire training data set at once. Online learning has become a promising technique for learning from continuous streams of data in many real-world applications. This survey aims to provide a comprehensive survey of the online machine learning literature through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the types of learning tasks and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) online supervised learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) online unsupervised learning where no feedback is available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.
234 citations
01 Jun 2014
TL;DR: “독립적” 토마스 켈리에 의해 발전된 전체증거 이론은 Bootstrapping이라는 심각한 교수님의 논
Abstract: 김기현 교수님에 따르면, 의견 불일치 문제에 대해서 최근 토마스 켈리에 의해 발전된 전체증거 이론은 Bootstrapping이라는 심각한 문제에 빠지게 된다. 하지만 저자는 김 교수님의 논증이 켈리가 받아들일 필요가 없는 원리에 기반하고 있다고 주장한다. 전체증거 이론은 심리적 증거가 비심리적 증거를 항상 함몰하는 것이 아니라고 주장한다. 하지만 이것이 김 교수님이 말하는 의미에서 심리적 증거가 비심리적 증거에 “독립적”임을 함축하지는 않는다.
171 citations
TL;DR: A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is presented in this paper, where the authors provide a comprehensive overview of the role of information fusion in biometrics.
Abstract: The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.
151 citations
TL;DR: Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.
Abstract: We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.
122 citations
Cites background or methods from "Improving cross-resolution face mat..."
...accuracies of all the other approaches that are reported in Table 4 are directly taken from [42]....
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...As in [42], images of Multi-PIE dataset are used for training and all the 29 subjects in the Choke Point database for testing....
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TL;DR: This survey provides a comprehensive review of established techniques and recent developments in HFR, and offers a detailed account of datasets and benchmarks commonly used for evaluation.
Abstract: Heterogeneous face recognition (HFR) refers to matching face imagery across different domains It has received much interest from the research community as a result of its profound implications in law enforcement A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years This survey provides a comprehensive review of established techniques and recent developments in HFR Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation We finish by assessing the state of the field and discussing promising directions for future research Display Omitted Provide a comprehensive review of established techniques in HFRProvide a thorough review of recent developments in HFROffer a detailed account of datasets and benchmarks commonly used for evaluationAssess the state of the field and discuss promising directions for future research
114 citations
Cites background from "Improving cross-resolution face mat..."
...[55] SVM SCface LPQ+SIFT 72× 72 24× 24 70%...
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...artefacts such as lighting change, motion-blur, shadows, non-frontal alignment and so on [43, 55]....
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...With few exceptions [43, 55], the majority of LR-HR studies simulate LR data by downsampling HR face images....
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...The outstanding research question for HFR is how to use these ideas to support cross-modal matching, which is just beginning to be addressed [24, 55]....
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...Finally [55] addressed LR-HR matching while simultaneously addressing the sparsity of annotated data by combining the ideas of co-training and transfer learning....
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TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Abstract: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.
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TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.
Abstract: We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0/98/ 7...%5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu
5,840 citations
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TL;DR: This paper presents a new approach to single-image superresolution, based upon sparse signal representation, which generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.
Abstract: This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework.
4,958 citations
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