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Facial expression recognition and histograms of oriented gradients: a comprehensive study.

Carcagnì P, Del Coco M, Leo M, Distante C - Springerplus (2015)

Bottom Line: This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose.A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline.The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out.

View Article: PubMed Central - PubMed

Affiliation: National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, Via della Libertà, 3, 73010 Arnesano , LE Italy.

ABSTRACT
Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human-robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose. In particular, this paper highlights that a proper set of the HOG parameters can make this descriptor one of the most suitable to characterize facial expression peculiarities. A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline. The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out. In the second experimental phase, different publicly available facial datasets were used to test the system on images acquired in different conditions (e.g. image resolution, lighting conditions, etc.). As a final phase, a test on continuous data streams was carried out on-line in order to validate the system in real-world operating conditions that simulated a real-time human-machine interaction.

No MeSH data available.


Related in: MedlinePlus

Examples of expression recognition in CK+ sequences: a from Neutral to Fearful and b from Neutral to Disgusted. For each sequence the first 3 and the last 5 frames are reported i.e. the frames containing the labelled expressions. The first row reports the facial images, the second row shows the ground-truth labels and finally, in the third row, the predicted expressions are pointed out
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Fig13: Examples of expression recognition in CK+ sequences: a from Neutral to Fearful and b from Neutral to Disgusted. For each sequence the first 3 and the last 5 frames are reported i.e. the frames containing the labelled expressions. The first row reports the facial images, the second row shows the ground-truth labels and finally, in the third row, the predicted expressions are pointed out

Mentions: In Fig . 13 two examplesof correct classification of CK+ image sequences are reported. In the first one, both the first 3 frames and the last 5 frames were correctly classified whereas in the second one an error occurred in the second frame but that was filtered by the overall decision rule.Fig. 13


Facial expression recognition and histograms of oriented gradients: a comprehensive study.

Carcagnì P, Del Coco M, Leo M, Distante C - Springerplus (2015)

Examples of expression recognition in CK+ sequences: a from Neutral to Fearful and b from Neutral to Disgusted. For each sequence the first 3 and the last 5 frames are reported i.e. the frames containing the labelled expressions. The first row reports the facial images, the second row shows the ground-truth labels and finally, in the third row, the predicted expressions are pointed out
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4628009&req=5

Fig13: Examples of expression recognition in CK+ sequences: a from Neutral to Fearful and b from Neutral to Disgusted. For each sequence the first 3 and the last 5 frames are reported i.e. the frames containing the labelled expressions. The first row reports the facial images, the second row shows the ground-truth labels and finally, in the third row, the predicted expressions are pointed out
Mentions: In Fig . 13 two examplesof correct classification of CK+ image sequences are reported. In the first one, both the first 3 frames and the last 5 frames were correctly classified whereas in the second one an error occurred in the second frame but that was filtered by the overall decision rule.Fig. 13

Bottom Line: This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose.A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline.The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out.

View Article: PubMed Central - PubMed

Affiliation: National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, Via della Libertà, 3, 73010 Arnesano , LE Italy.

ABSTRACT
Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human-robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose. In particular, this paper highlights that a proper set of the HOG parameters can make this descriptor one of the most suitable to characterize facial expression peculiarities. A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline. The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out. In the second experimental phase, different publicly available facial datasets were used to test the system on images acquired in different conditions (e.g. image resolution, lighting conditions, etc.). As a final phase, a test on continuous data streams was carried out on-line in order to validate the system in real-world operating conditions that simulated a real-time human-machine interaction.

No MeSH data available.


Related in: MedlinePlus