Facial expression recognition using facial characteristic points and Gini index

Perveen, Nazil; Gupta, Shubhrata; Verma, Kesari

doi:10.1109/sces.2012.6199086

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…In this case, the number of rules given by the decision tree trained on the JAFFE database is 36 rules, while after using the pruning phase the number of rules is reduced to 24 rules, therefore, the recognition rate decreases up to 84.03%. Some rules of decision tree trained on the JAFFE database are given below: The last column in Table III represents the result of Perveen et al [9] on thirty particular images from the JAFFE database.…”

Section: B Resultsmentioning

confidence: 99%

“…how a facial expression on the frontal face image can be identified. Previous researches [4], [9], [11], [14] focused their work on three parts on the face (eyebrows, eyes, mouth). Beside in [6], all muscles on the face are used to encode a facial expression; they used an important number of features.…”

Section: Facial Expression Recognition Systemmentioning

confidence: 99%

“…Perveen et al (2012) [9] focused their work on three regions (eyebrows, eyes, mouth) to define an emotion. First, they calculate the characteristic points of the face, and then they try to evaluate some animation parameters such as: openness of eyes, width of eyes, height of eyebrows, opening of mouth, and width of mouth (Fig.…”

mentioning

confidence: 99%

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Facial Expression Recognition Using Decision Trees

Salmam

Madani

Kissi

2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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Emotion recognition from facial expressions is generally performed in three steps: face detection, features extraction and classification of expressions. The present work focuses on two points: Firstly, a new extraction method is presented based on the geometric approach. This method consists of calculating six distances in order to measure parts of the face that better describe a facial expression. Secondly, an automatic supervised learning method called decision tree is applied on two databases (JAFEE and COHEN), in order to have a facial expressions classifying system with seven possible classes (six basic emotions plus neutral); this system uses as input the six distances previously calculated (using Euclidian ,Manhattan or Minkowski distance) for each face. Our results achieved a recognition rate of 89.20% and 90.61% respectively in JAFFE and COHEN database.

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Section: B Resultsmentioning

confidence: 99%

Section: Facial Expression Recognition Systemmentioning

confidence: 99%

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Facial Expression Recognition Using Decision Trees

Salmam

Madani

Kissi

2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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“…There are a plethora of works that aim to facilitate the way of recognizing emotions from facial expression using static [10,11,12] or dynamic images [13,14,15,16,17,18,9].…”

Section: Related Workmentioning

confidence: 99%

“…Perveen et al [10] have focused their work on three regions (eyebrows, eyes, mouth) to define an emotion from static images. First, they calculated the characteristic points of the face, then they tried to evaluate some animation parameters such as: the openness of eyes, the width of eyes, the height of eyebrows, the opening of mouth, and the width of mouth.…”

Section: Related Workmentioning

confidence: 99%

Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network

Salmam¹,

Madani²,

Kissi³

2018

IJECE

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The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database.

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