Visual tracking and recognition using probabilistic appearance manifolds

Lee, Kuang-Chih; Ho, Jeffrey; Yang, Ming–Hsuan; Kriegman, David

doi:10.1016/j.cviu.2005.02.002

Cited by 218 publications

(155 citation statements)

References 44 publications

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“…They presented a maximum a posteriori formulation for face recognition, which integrates the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. Their approach was extended for simultaneously tracking and recognizing faces in video [65], achieving the recognition rate of 98.8% on a data set of 20 subjects. However, the appearance model in these works was learned by a batch training process from short video clips, which is not practical for large number of lengthy video sequences.…”

Section: Temporal Model Based Approachesmentioning

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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Section: Temporal Model Based Approachesmentioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Face Recognition and Retrieval in Video

Shan¹

2010

Video Search and Mining

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“…Early video-based face recognition algorithms were frame based however, recent techniques match video sequences and use temporal coherence between the query and database videos in addition to the spatial information contained in individual frames [13]. Recognition systems can be trained offline once only [13], or even online [14]. Hybrid training approaches learn generic or specific face models offline in a batch mode and continuously updates the models during online recognition [15].…”

Section: Literature Reviewmentioning

confidence: 99%

User Verification by Combining Speech and Face Biometrics in Video

Naseem¹,

Mian

2008

Advances in Visual Computing

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Abstract. In this paper, physiological biometrics from face are combined with behavioral biometrics from speech in video to achieve robust user authentication. The choice of biometrics is motivated by user convenience and robustness to forgery as it is hard to simultaneously forge these two biometrics. We used the Mel Frequency Cepstral Coefficients for text-independent speaker recognition and local scale invariant features for video-based face recognition. Results of the two classifiers were fused using a weighted sum rule and an equal error rate of 0.6% was achieved on the VidTIMIT audio-visual database. We also performed identification experiments and achieved a combined identification rate of 99.13% on the same database.

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“…A mixture of distributions is used in [24] to model the observed value of each pixel, where the occluded pixels are characterised by having an abrupt difference with respect to a uniform distribution. Contextual information is exploited in [25,27]. These methods have better performance in terms of analysing occlusion situations but tracking errors are observed to frequently occur and propagate away.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic integrated object recognition and tracking framework

Serratosa

Alquézar

Amézquita

2012

Expert Systems with Applications

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Abstract. This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB colour features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.

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Visual tracking and recognition using probabilistic appearance manifolds

Cited by 218 publications

References 44 publications

Face Recognition and Retrieval in Video

Face Recognition and Retrieval in Video

User Verification by Combining Speech and Face Biometrics in Video

A probabilistic integrated object recognition and tracking framework

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