Differential optical flow applied to automatic facial expression recognition

Sánchez, Antonio Ruiz; Ruiz, J. V.; Moreno, Ana Belén; Montemayor, Antonio S.; Hernández, José Juan Carbajal; Pantrigo, Juan José

doi:10.1016/j.neucom.2010.07.017

Cited by 56 publications

(28 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

show abstract

“…Recognition rate IMG [23] 89.35 % LBP [23] 96.57 % Gabor [23] 95.38 % Zernike Moment [24] 73.20 % Radial encoding of local Gabor features [25] 91.51% Pyramid histogram of orientated gradient [26] 96.33% Differential optical flow [27] 95.45% Proposed methods: "PFI" + SIFT 98.31 % It is obvious that the suggested approach, applying Perceived Facial Images "PFI" and SIFT, leads to the best classification rate (98.31%), which is higher than the other methods.…”

Section: Systemsmentioning

confidence: 99%

Facial Expression Recognition Based on Perceived Facial Images and Local Feature Matching

Boughrara

Chen

Amar

et al. 2013

Image Analysis and Processing – ICIAP 2013

View full text Add to dashboard Cite

Abstract. Facial expression recognition is to determine the emotional state of the face regardless of its identity. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. This paper presents a biological vision-based facial description, called Perceived Facial Images "PFI" applied to facial expression recognition. For the classification step, Scale Invariant Feature Transform "SIFT" is used to extract a local feature in images. Then, a matching computation is processed between a testing image and all train images for recognizing facial expression. To evaluate, the proposed approach is tested on the GEMEP FERA 2011 database and the Cohn-Kanade Facial Expression database. To compare, the developed algorithm achieves better experimental results than the other approaches in the literature.

show abstract

“…On one hand the Holistic approaches either use the whole face [7,25] or partial information about regions in the face like mouth or eyes [14,21,31]. For the motion extraction methods, the features are extracted by analysing motion vectors: holistically [8,29], and locally [26,34,39] obtained.…”

Section: Computational Perspectivementioning

confidence: 99%

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Sánchez¹,

Hernández²,

Orellana³

2012

Visual Cortex - Current Status and Perspectives

View full text Add to dashboard Cite

Differential optical flow applied to automatic facial expression recognition

Cited by 56 publications

References 15 publications

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

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

Facial Expression Recognition Based on Perceived Facial Images and Local Feature Matching

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Contact Info

Product

Resources

About