Face recognition using temporal image sequence

Yamaguchi, Osamu; Fukui, Kiichi; Maeda, Kenichi

doi:10.1109/afgr.1998.670968

Cited by 378 publications

(361 citation statements)

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“…Generally speaking, research on face recognition focuses much on exploring the distribution [5] or structure of the data sets [6][7][8] and designing the between-set distance [9,2], while in the literature of person reidentification, more attention has been paid to feature representation [3,10,4]. Such a phenomenon to some extent is due to the differences between these two categories: usually human bodies have greater appearance variations and occlusions than faces, causing difficulties for feature representation.…”

Section: Related Workmentioning

confidence: 99%

Set Based Discriminative Ranking for Recognition

Minoh

Lao

2012

Computer Vision – ECCV 2012

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Abstract.Recently both face recognition and body-based person reidentification have been extended from single-image based scenarios to video-based or even more generally image-set based problems. Set-based recognition brings new research and application opportunities while at the same time raises great modeling and optimization challenges. How to make the best use of the available multiple samples for each individual while at the same time not be disturbed by the great within-set variations is considered by us to be the major issue. Due to the difficulty of designing a global optimal learning model, most existing solutions are still based on unsupervised matching, which can be further categorized into three groups: a) set-based signature generation, b) direct set-to-set matching, and c) between-set distance finding. The first two count on good feature representation while the third explores data set structure and set-based distance measurement. The main shortage of them is the lack of learning-based discrimination ability. In this paper, we propose a set-based discriminative ranking model (SBDR), which iterates between set-to-set distance finding and discriminative feature space projection to achieve simultaneous optimization of these two. Extensive experiments on widely-used face recognition and person re-identification datasets not only demonstrate the superiority of our approach, but also shed some light on its properties and application domain.

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Section: Related Workmentioning

confidence: 99%

Set Based Discriminative Ranking for Recognition

Minoh

Lao

2012

Computer Vision – ECCV 2012

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“…This is based on the assumption that images are drawn from some distributions on the underlying face pattern manifold, and normally statistical learning algorithms are adopted to model the distribution. Recently, following the mutual subspace method [110], many approaches build a compact model of the distribution by representing each image set as a linear subspace, and measure their similarity using the canonical angles [110,82,59]. In the following sections, we discuss these two groups of approaches: statistical model-based and mutual subspace-based, respectively.…”

Section: Image-set Matching Based Approachesmentioning

confidence: 99%

“…Yamaguchi et al [110] introduced the Mutual Subspace Method (MSM), where each image set is represented by the linear subspace spanned by the principal components of the images. The similarity between image sets is measured by the smallest principal angles between subspaces.…”

Section: Mutual Subspace-based Approachesmentioning

confidence: 99%

“…By taking into account temporal coherence, face dynamics (such as non-rigid facial expressions and rigid head movements) within the video sequence are modeled and exploited to enforce recognition. The third class [110,93,10,59,106] also uses all face images, but does not assume temporal coherence between consecutive images; the problem was treated as an image-set matching problem. The distributions of face images in each set are modeled and compared for recognition, and the existing work can be further divided into statistical model-based and mutual subspace-based methods (see Section 3.3 for details).…”

Section: Face Recognition In Videomentioning

confidence: 99%

“…Method Key-frame based Approaches [90], [40], [47], [114], [100], [17], [115], [31], [78], [85], [98], [101], [118] Temporal Model based Approaches [74], [73], [72], [75], [18], [24], [67], [69], [68], [122], [120], [123], [121], [64], [65], [66], [79], [55], [2], [43], [50], [49] Image-Set Matching based Approaches Statistical model-based [93], [4], [96], [7], [10], [6], [9] Mutual subspace-based [110], [90], [35], [82], [108], [56], [57], [5]<...>…”

Section: Categorymentioning

confidence: 99%

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Face Recognition and Retrieval in Video

Shan¹

2010

Video Search and Mining

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Abstract. Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.

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