Emotion recognition using PHOG and LPQ features

Dhall, Abhinav; Asthana, Akshay; Goecke, Roland; Gedeon, Tom

doi:10.1109/fg.2011.5771366

Cited by 175 publications

(90 citation statements)

References 25 publications

(39 reference statements)

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“…To make further analysis memory and computationally inexpensive, a key-interest point selection approach, similar to concept frame selection in affect analysis [29], is used. For a video V, a total of K detected interest points are clustered using the Approximate Nearest Neighbour (ANN) algorithm and Fig.…”

Section: B Holistic Body Movementmentioning

confidence: 99%

Relative Body Parts Movement for Automatic Depression Analysis

Joshi

Dhall

Goecke

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Abstract-In this paper, a human body part motion analysis based approach is proposed for depression analysis. Depression is a serious psychological disorder. The absence of an (automated) objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Researchers in the affective computing community have approached the depression detection problem using facial dynamics and vocal prosody. Recent works in affective computing have shown the significance of body pose and motion in analysing the psychological state of a person. Inspired by these works, we explore a body parts motion based approach. Relative orientation and radius are computed for the body parts detected using the pictorial structures framework. A histogram of relative parts motion is computed. To analyse the motion on a holistic level, space-time interest points are computed and a bag of words framework is learnt. The two histograms are fused and a support vector machine classifier is trained. The experiments conducted on a clinical database, prove the effectiveness of the proposed method.

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Section: B Holistic Body Movementmentioning

confidence: 99%

Relative Body Parts Movement for Automatic Depression Analysis

Joshi

Dhall

Goecke

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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“…An adequate facial representation is central for effective affect recognition as the classification performance is limited by the quality and relevance of the features used in the represen-Discrete Continuous Global Local Global Local P [19], [40], [17] [1], [38], [33], [36], [28], [34], [13], [OurWork] --N [21], [14] [9], [6], [24], [31], [5], [14] [25] [32], [14], [8], [25][30], [OurWork] Table 1: Summary of appearance representations used for affect recognition. The representations are categorised by type (local vs. global), nature of data (naturalistic, N vs. posed, P) and affect modeling (discrete vs. continuous).…”

Section: Introductionmentioning

confidence: 99%

Local Zernike Moment Representation for Facial Affect Recognition

Sarıyanidi¹,

Güneş²,

Gokmen³

et al. 2013

Procedings of the British Machine Vision Conference 2013

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In this paper, we propose to use local Zernike Moments (ZMs) for facial affect recognition and introduce a representation scheme based on performing non-linear encoding on ZMs via quantization. Local ZMs provide a useful and compact description of image discontinuities and texture. We demonstrate the use of this ZM-based representation for posed and discrete as well as naturalistic and continuous affect recognition on standard datasets, and show that ZM-based representations outperform well-established alternative approaches for both tasks. To the best of our knowledge, the performance we achieved on CK+ dataset is superior to all results reported to date.

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“…SVMs have been widely used in the literature to model classification problems including facial expression recognition [30], [34], [19]. Provided a set of training samples, an SVM transforms the data samples using a non-linear mapping to a higher dimension with the aim to determine a hyperplane that partitions the data by class or labels.…”

Section: Algorithm 1: Compute Hdtpmentioning

confidence: 99%

“…In addition, facial expressions have been analysed [16] and classified [17] using TS imaging. Commonly, VS imaging has been used for modelling affective computing problems such as depression [18], emotion [19], and pain analysis [20]. However from our understanding, the literature has not developed computational models for stress recognition using both TS and VS imaging together yet.…”

Section: Introductionmentioning

confidence: 99%

Modeling Stress Using Thermal Facial Patterns: A Spatio-temporal Approach

Sharma

Dhall

Gedeon

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Abstract-Stress is a serious concern facing our world today, motivating the development of better objective understanding using non-intrusive means for stress recognition. The aim for the work was to use thermal imaging of facial regions to detect stress automatically. The work uses facial regions captured in videos in thermal (TS) and visible (VS) spectra and introduces our database ANU StressDB. It describes the experiment conducted for acquiring TS and VS videos of observers of stressed and not-stressed films for the ANU StressDB. Further, it presents an application of local binary patterns on three orthogonal planes (LBP-TOP) on VS and TS videos for stress recognition. It proposes a novel method to capture dynamic thermal patterns in histograms (HDTP) to utilise thermal and spatio-temporal characteristics associated in TS videos. Individual-independent support vector machine classifiers were developed for stress recognition. Results show that a fusion of facial patterns from VS and TS videos produced significantly better stress recognition rates than patterns from only VS or TS videos with p < 0.01. The best stress recognition rate was 72% and it was obtained from HDTP features fused with LBP-TOP features for TS and VS videos, respectively.

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Emotion recognition using PHOG and LPQ features

Cited by 175 publications

References 25 publications

Relative Body Parts Movement for Automatic Depression Analysis

Relative Body Parts Movement for Automatic Depression Analysis

Local Zernike Moment Representation for Facial Affect Recognition

Modeling Stress Using Thermal Facial Patterns: A Spatio-temporal Approach

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