An on-line learning method for face association in personal photo collection

Presti, Liliana Lo; Cascia, Marco La

doi:10.1016/j.imavis.2012.02.011

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…There is an extensive literature on face recognition [37], [15] and on facial expression analysis [35], [7]. Here we focus on works that try to embed the temporal structure of the sequence of facial expression either in the feature representation step or in the emotion-model.…”

Section: Related Workmentioning

confidence: 99%

“…There is an extensive literature on face recognition [37], [15] and on facial expression analysis [35], [7].…”

Section: Related Workmentioning

confidence: 99%

“…The goal is that of recognizing between pain and no-pain events. The videos were annotated on a frame-per-frame basis with the Prkachin and Solomon pain intensity (PSPI) score [20], which ranges in [0,15] where 0 means no pain, while a value greater than 0 indicates a certain intensity of pain. However, our method works on temporal windows and the validation requires a temporal window-based annotation.…”

Section: Data and Validation Protocolsmentioning

confidence: 99%

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Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

Presti

Cascia

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI) system. The temporal dynamics of such sequence of descriptors are represented by means of a Hankel matrix.\ud The paper presents different strategies to compute dynamics-based representation of a sequence of FID, and reports classification accuracy values of the proposed representations within different standard classification frameworks. The representations have been validated in two very challenging application domains: emotion recognition and pain detection. Experiments on two publicly available benchmarks and comparison with state-of-the-art approaches demonstrate that the dynamics-based FID representation attains competitive performance when off-the- shelf classification tools are adopted

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Section: Related Workmentioning

confidence: 99%

“…There is an extensive literature on face recognition [37], [15] and on facial expression analysis [35], [7].…”

Section: Related Workmentioning

confidence: 99%

Section: Data and Validation Protocolsmentioning

confidence: 99%

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Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

Presti

Cascia

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…Face detection [31], face recognition [37], [14] and facial expression analysis [6] have been deeply studied in past years, resulting in a vast literature reviewed in [35], [27]. In this section, we focus on works that embed the temporal structure of the face image sequence in the feature representation or in the emotion model.…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Hankel Matrices for Face Emotion Recognition

Presti

Cascia

2015

Image Analysis and Processing — ICIAP 2015

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Abstract. In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.

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“…Most detection algorithms can be used as an intermediate step for retrieving images in CBIR [99,90], such as recognized faces [187,121] and events [37], among others.…”

Section: Image Retrievalmentioning

confidence: 99%

A survey on automatic techniques for enhancement and analysis of digital photography

2013

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The fast growth in the consumer digital photography industry during the past decade has led to the acquisition and storage of large personal and public digital collections containing photos with different quality levels and redundancy, among other aspects. This naturally increased the difficulty in selecting or modifying those photos. Within the above context, this survey focuses on systematically reviewing the state-of-art on techniques for the enhancement and analysis of digital photos. Nevertheless, it is not within the scope of this survey to review image quality metrics for evaluating degradation due to compression, digital sensor noise, and affine issues. Assuming the photos have good quality in those aspects, this review is centered on techniques that might be useful to automate the task of selecting photos from large collections or to enhance the visual aspect of imperfect photos by using some perceptual measure.

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An on-line learning method for face association in personal photo collection

Cited by 8 publications

References 42 publications

Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

Ensemble of Hankel Matrices for Face Emotion Recognition

A survey on automatic techniques for enhancement and analysis of digital photography

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