Real-time face recognition for human-robot interaction

Ulloa, Christyan Cruz; Sucar, L. Enrique; Morales, Eduardo F.

doi:10.1109/afgr.2008.4813386

Cited by 32 publications

(20 citation statements)

References 11 publications

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“…Fernandez and Vicente [36] combine Harris-Laplace and Difference-of-Gaussian detectors to detect both corner and blob structures in face images and use SIFT descriptors to represent them. In [37][38][39], salient regions are firstly identified from face images, and then SIFT features are extracted in each region. A modified keypoint descriptor and a redundant keypoint removal scheme are proposed for face recognition in [40,41].…”

Section: Introductionmentioning

confidence: 99%

Face recognition based on the multi-scale local image structures

Geng

Jiang

2011

Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

Face recognition based on the multi-scale local image structures

Geng

Jiang

2011

Pattern Recognition

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“…HRI has become an extensive field of research in the machine vision and robotics community (e.g., see Goodrich and Schultz [2007], Argall et al [2009], and Kober et al [2013] for recent surveys). Some recent research efforts have used online incremental learning, for vision-based HRI tasks, with widely dispersed learning methods [Raducanu and Vitria 2007;Steil and Wersing 2006;Moller et al 2005;Cruz et al 2008;Kawewong et al 2011;Kankuekul et al 2012]. To the best of our knowledge, ours is the first method applied to HRI using binary feedback for online multiclass prediction problems.…”

Section: Background and Related Workmentioning

confidence: 95%

Efficient Interactive Multiclass Learning from Binary Feedback

Ngo

Luciw

Nagi

et al. 2014

ACM Trans. Interact. Intell. Syst.

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We introduce a novel algorithm called upper confidence-weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In UCB, each instance is classified using both score and uncertainty. For a given instance in the sequence, the algorithm might guess its class label primarily to reduce the class uncertainty. This is a form of informed exploration, which enables the performance to improve with lower sample complexity compared to the case without exploration. Combining UCB with SCW leads to the ability to deal well with noisy and nonseparable data, and state-of-the-art performance is achieved without increasing the computational cost. A potential application setting is human-robot interaction (HRI), where the robot is learning to classify some set of inputs while the human teaches it by providing only binary feedback-or sometimes even the wrong answer entirely. Experimental results in the HRI setting and with two benchmark datasets from other settings show that UCWL outperforms other state-of-the-art algorithms in the online binary feedback setting-and surprisingly even sometimes outperforms state-of-the-art algorithms that get full feedback (e.g., the true class label), whereas UCWL gets only binary feedback on the same data sequence.

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“…The candidates c s are furthermore confined to two or three c s at each scale s under an overlap condition. The overlap condition is defined as follows: Let the candidate positions x 1 I and x 2 I be close to each other. Let the size of an area O 1 overlapping x 1 I and x 2 I be A O 1 (Figure 8).…”

Section: Select Candidate Positionsmentioning

confidence: 99%

“…For example, in a security system, it is an important preprocessing for identifying or tracking moving persons on an image, taken with a surveillance camera in public places. It may also often be used for acquiring information about a person in order to make more sophisticated dialogs between persons and life support robots [1].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale elastic graph matching for face detection

Sato

Kuriya

2013

EURASIP J. Adv. Signal Process.

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We propose a multi-scale elastic graph matching (MS-EGM) algorithm for face detection, in which the conventional EGM is improved with two simple image processing techniques of the Gabor wavelet-based pyramid and the weak Gabor feature elimination. It is expected to solve difficulties of the real-time process in the conventional EGM. The Gabor wavelet-based pyramid effectively reduces not only the computational cost of the Gabor filtering but also the computational complexity of feature representation of a model face, preserving the facial information. The elimination of the weak Gabor feature extracted from an input image facilitates an accuracy of the Gabor feature similarity computations as unexpected. We then test that the MS-EGM can be capable of rapid face detection processing while achieving a high correct detection rate, comparable to the AdaBoost Haar-like (HL) feature cascade. We also show that the MS-EGM has strong robustness to the image of a face occluded with sunglasses and scarfs because of topologically preserved feature representations.

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Real-time face recognition for human-robot interaction

Abstract: The ability to recognize people is a key element for improving human-robot

Cited by 32 publications

References 11 publications

Face recognition based on the multi-scale local image structures

Face recognition based on the multi-scale local image structures

Efficient Interactive Multiclass Learning from Binary Feedback

Multi-scale elastic graph matching for face detection

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