An analysis of facial expression recognition under partial facial image occlusion

Kotsia, Irene; Buciu, Ioan; Pitas, Ioannis

doi:10.1016/j.imavis.2007.11.004

Cited by 205 publications

(162 citation statements)

References 30 publications

(72 reference statements)

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“…We first present the comparison results of our descriptors with other state-of-the-art descriptors, as shown in Table 1. The state-of-the-art descriptors we compare include Gabor3×4 [6], Gabor5×8 [19], LBP(LBP256) [8], ULBP(LBP59) [9], HOG [10], and LGBPHS [20].…”

Section: Results On Jaffe Databasementioning

confidence: 99%

Hybrid Facial Representations for Emotion Recognition

Yun¹

2013

ETRI Journal

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Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixelbased descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

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Section: Results On Jaffe Databasementioning

confidence: 99%

Hybrid Facial Representations for Emotion Recognition

Yun¹

2013

ETRI Journal

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“…7. I/O and BP error for the system using a merged image lips, sometimes, they are pressed tightly in both cases [38]. From 12 images of disgust, 10 of them were successfully recognized, while the remaining 2 were classified as sadness, in this case we could think that the confusion is produced by the similarity of the mouth, in these two face emotions, the mouth shape forms a bow that arches.…”

Section: A Facial Emotion Detection System Using the Complete Mergedmentioning

confidence: 93%

Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network

Rázuri

Sundgren

Rahmani

et al. 2013

2013 12th Mexican International Conference on Artificial Intelligence

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Abstract-This paper focuses on a system of recognizing human's emotion from a detected human's face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human's expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

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“…In this study only the magnitude features are used to represent the facial Gabor features. Changing the facial positions will cause only slight variations in the magnitude of the Gabor wavelet, while the phase of the Gabor wavelet output is very sensitive to the position of the face [20]. The 200×200 pixels in the Gabor image produce a large number of features is 40000 feature vectors.…”

Section: ) Appearance Features Extractionmentioning

confidence: 99%

Automatic Facial Expression Recognition Based on Hybrid Approach

Bermani¹,

Ghalwash²,

Youssif³

2012

IJACSA

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Abstract-The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance). Expressions recognition is performed by using radial basis function (RBF) based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness) in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

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An analysis of facial expression recognition under partial facial image occlusion

Cited by 205 publications

References 30 publications

Hybrid Facial Representations for Emotion Recognition

Hybrid Facial Representations for Emotion Recognition

Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network

Automatic Facial Expression Recognition Based on Hybrid Approach

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