Emotion Recognition System by a Neural Network Based Facial Expression Analysis

Filko, Damir; Martinović, Goran

doi:10.7305/automatika.54-2.73

Cited by 36 publications

(12 citation statements)

References 16 publications

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“…Emotions are visualized through various indicators in humans, many of these indicators have been previously analyzed to provide affective knowledge to machines, focusing on facial expressions [5], [6], vocal features [7], [8], [9], body movements and postures [10], [11], [12], [13] and the integration of all of them in emotion analysis systems [14], [15], [16]. But human beings cannot always hope that robots may be able to react in a timely and sensible manner, especially if they haven't be able to recover all the affective information through their sensors.…”

Section: Introductionmentioning

confidence: 99%

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Rázuri¹,

Sundgren²,

Rahmani³

et al. 2015

ijarai

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Abstract-For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such properties of sound to recognize emotions. This work evaluated six different kinds of classifiers to predict six basic universal emotions from non-verbal features of human speech. The classification techniques used information from six audio files extracted from the eNTERFACE05 audiovisual emotion database. The information gain from a decision tree was also used in order to choose the most significant speech features, from a set of acoustic features commonly extracted in emotion analysis. The classifiers were evaluated with the proposed features and the features selected by the decision tree. With this feature selection could be observed that each one of compared classifiers increased the global accuracy and the recall. The best performance was obtained with Support Vector Machine and bayesNet.

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Section: Introductionmentioning

confidence: 99%

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Rázuri¹,

Sundgren²,

Rahmani³

et al. 2015

ijarai

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“…The authors of [29] employed artificial neural networks (ANNs) to recognize different types of facial. However, an ANN is a black box and has incomplete capability to explicitly categorize possible fundamental relationships [30].…”

Section: Related Workmentioning

confidence: 99%

“…Most of these methods exploited other information instead of employing intensity to overcome the problems due to noise and illumination change [28]. However, the performance of these methods still degrades in non-monotonic illumination change, noise variation, change in pose and expression conditions [29].…”

Section: Related Workmentioning

confidence: 99%

Depth Camera-Based Facial Expression Recognition System Using Multilayer Scheme

Ṣiddiqi

Ali

Sattar

et al. 2014

IETE Technical Review

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The analysis of a facial expression in telemedicine and healthcare plays a significant role in providing sufficient information about patients such as stroke and cardiac in monitoring their expressions for better management of their diseases. Facial expression recognition (FER) improves the level of interaction between human-tohuman communications. The human face has a major contribution for such types of communications, which consists of lips, eyes and forehead that are considered the most informative features for FER. There are some parameters that make FER a challenging task that includes high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. Moreover, most of the previous works used existing available standard datasets and all the datasets were pose-based datasets, and they have some privacy issues because of utilizing video (RGB) cameras. Accordingly, this work presents a multilayer scheme for FER to handle these issues. In the proposed FER system, we utilized a depth camera in order to solve the privacy concerns, and the accuracy of this camera is not affected by any kind of environmental parameters. Similarly, the depth camera automatically detects and extracts the faces based on the distance between the camera and subject. For global and local feature extraction, principal component analysis (PCA) and independent component analysis (ICA) were used. A hierarchical classifier was used, where the expression category was recognized at the first level, followed by the actual expression recognition at the second level. For the entire experiments, an n-fold cross-validation scheme (based on subjects) was employed. The proposed FER system achieved a significant improvement in accuracy (98.0%) against the existing methods.

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“…Past works on neural network based affective computing have focused on the segmentation of single facial expression into finer sub-components, which can be achieved via the added principal component analysis (PCA) [9] or the complex feature pre-processing engineering, e.g., the introduction of Sobel filters [10]. However, the complex in emotional representation demands affective analysis to move beyond the single label categorisation.…”

Section: Previous Work On Affective Learningmentioning

confidence: 99%

Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

2018

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Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners' emotional stability in jail, can be directly benefited from our framework. Abstract:To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e., Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty-which can be quantified in Uncertainty Flow-is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our proposed Uncertainty Flow provides a glimpse of future in continuous, uncertain, and multi-label affective computing.

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Emotion Recognition System by a Neural Network Based Facial Expression Analysis

Cited by 36 publications

References 16 publications

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Speech emotion recognition in emotional feedback for Human-Robot Interaction

Depth Camera-Based Facial Expression Recognition System Using Multilayer Scheme

Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

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