Facial Expression Recognition Using Decision Trees

Salmam, Fatima Zahra; Madani, Abdellah; Kissi, Mohamed

doi:10.1109/cgiv.2016.33

Cited by 64 publications

(23 citation statements)

References 13 publications

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“…Lee et al [29] use an SVM to classify the facial expression images in CK+ database [30,31] and JAFFE 1 database [32] with performance accuracies of 94.3% and 92.22%, respectively. A decision tree approach was applied to classify expressions in CK+ database by Salmam et al [33] and the accuracy was 90%. In CNN, the fully connected layer is a classifier actually.…”

mentioning

confidence: 99%

Facial expression recognition using optimized active regions

Sun

Huang

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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Facial expression, which is a fundamental mode of transporting human's emotions, plays a significant role in our daily communication. Facial expression recognition is a complex and interesting problem, and finds its applications in driver safety, health-care, humancomputer interaction etc. Due to its wide range of applications, facial expression recognition has received substantial attention among the researchers in the area of computer vision [1-3]. Although a number of novel methodologies have been proposed in recent years, recognizing facial expression with high accuracy and speed remains challenging due to the complexity and variability of facial expressions. For facial expression recognition problems, the general recognition method appeared in previous work can be divided into two major steps, face representation and classifier construction. In the first step, features related to facial expression are extracted from images. Some of the features are hand-designed [4-6], whereas others are learnt from training images [7-9]. Then, the dimensionality of the features is reduced to facilitate an efficient classification and enhance the generalization capability. The universal expressions which are mentioned in the papers are usually anger, disgust, fear, joy (or happiness), sadness and surprise [1, 10], whereas some researchers add neutral as the seventh

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mentioning

confidence: 99%

Facial expression recognition using optimized active regions

Sun

Huang

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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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%

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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“…CRT [4] uses recursive segmentation to split current sample set into two sub‐sets to find the best classification results. Gini impurity

G false(T false)

is used for measuring the best splits of data, i.e.…”

Section: M‐crt Decisionmentioning

confidence: 99%

“…Existing FER approaches [1–3] focus on the extraction of facial geometric features. Recently, Zahra et al [4] propose classification and regression tree (CRT) model to classify facial expression by calculating six distances between pertinent areas on the face. However, CRT only considers the local geometric information of facial landmarks and ignores the global structure information.…”

Section: Introductionmentioning

confidence: 99%

Modified classification and regression tree for facial expression recognition with using difference expression images

2017

Electron. lett.

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This study presents a modified classification and regression tree (M‐CRT) framework based on difference expression images, to address the facial expression recognition (FER) problem. The authors firstly obtain facial expressional details by calculating the difference between the images of basic expressions and images of neutral expression, which reflect the information irrelevant to identities. Local binary patterns and supervised descent method are, respectively, used to obtain the global and local features from difference expression images. M‐CRT model is developed for FER, which uses recursive segmentation to find the best classification decision according to the attributes of the global and local features, respectively. Compared with traditional methods, M‐CRT can simultaneously maximise intra‐class purity and distance between classes, which improves the discriminating power for classification. Experimental results on Japanese Female Facial Expression and CK+ database verify the effectiveness of their method.

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Facial Expression Recognition Using Decision Trees

Cited by 64 publications

References 13 publications

Facial expression recognition using optimized active regions

Facial expression recognition using optimized active regions

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

Modified classification and regression tree for facial expression recognition with using difference expression images

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