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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

Bar diagram of processing time for descriptor computation and SVM prediction stages respect to LBP, CLBP and SWLD descriptors
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Fig8: Bar diagram of processing time for descriptor computation and SVM prediction stages respect to LBP, CLBP and SWLD descriptors

Mentions: The computational cost prospective has to be discussed too. The values (processing time for the descriptor computation and the SVM prediction), reported in Table 2 and illustrated in the bar diagram of Fig. 8, have been computed considering an average of 500 frames employing an Intel i7 processor class based Linux machine. The analysis of these results shows as the LBP descriptor allows to save about 0.4 ms with respect to the HOG one. Nevertheless, the processing time for the face detection and registration step (41, 9 ms) is 2 magnitude greater than the discussed times making this advantage quite negligible.Fig. 8


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

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

Bar diagram of processing time for descriptor computation and SVM prediction stages respect to LBP, CLBP and SWLD descriptors
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig8: Bar diagram of processing time for descriptor computation and SVM prediction stages respect to LBP, CLBP and SWLD descriptors
Mentions: The computational cost prospective has to be discussed too. The values (processing time for the descriptor computation and the SVM prediction), reported in Table 2 and illustrated in the bar diagram of Fig. 8, have been computed considering an average of 500 frames employing an Intel i7 processor class based Linux machine. The analysis of these results shows as the LBP descriptor allows to save about 0.4 ms with respect to the HOG one. Nevertheless, the processing time for the face detection and registration step (41, 9 ms) is 2 magnitude greater than the discussed times making this advantage quite negligible.Fig. 8

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