Facial Expression Recognition Using Local Facial Features

2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015

Recently, recognition of naturalistic expressions known as spontaneous facial expressions has attracted attention from researchers due to its significant application in behavioral and clinical research. Currently, most of the work consider recognition of posed expressions. In this paper, we propose a spatio-temporal feature extraction method, Spatio-Temporal Texture Map (STTM), for recognition of spontaneous expressions and compare its performance against that of state-ofthe-art feature extraction methods. Both appearancebased and geometry-based feature extraction approaches are considered for comparisons against STTM. The appearance-based techniques considered are Volume Local Binary Pattern (VLBP) and Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) whereas a multi-view tree-based face detector is considered as a geometry-based technique. Support Vector Machine (SVM) is used as the classifier where the extracted features are classified into classes of naturalistic expressions. The feature extraction methods are evaluated over the spontaneous facial expression data from CASME II database. Experimental results show that STTM is capable of recognizing spontaneous expressions and outperforming the other methods in terms of recognition rate, accuracy and computational cost.

Many researchers adopt Local Binary Pattern for pattern analysis. However, the long histogram created by Local Binary Pattern is not suitable for large-scale facial database. This paper presents a simple facial pattern descriptor for facial expression recognition. Local pattern is computed based on local gradient flow from one side to another side through the center pixel in a 3x3 pixels region. The center pixel of that region is represented by two separate two-bit binary patterns, named as Local Gradient Pattern-LGP for that pixel. LGP pattern is extracted from each pixel. Facial image is divided into 81 equal sized blocks and the histograms of local LGP features for all 81 blocks are concatenated to build the feature vector. Experimental results prove that the proposed technique along with Support Vector Machine is effective for facial expression recognition.

Signal Processing: Image Communication, 2017

Automatic facial expression analysis is a challenging topic in computer vision due to its complexity and its important role in many applications such as human-computer and social interaction. This paper presents a Facial Expression Recognition (FER) method based on an automatic and more efficient facial decomposition into regions of interest (ROI). First, seven ROIs, representing more precisely facial components involved in expression of emotions (left eyebrow, right eyebrow, left eye, right eye, between eyebrows, nose and mouth), are extracted using the positions of some landmarks detected by IntraFace (IF).

Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in the applications of human-computer interactions. The vital component of any successful expression recognition system is an effective facial representation from face images. In this paper, we have derived an appearance-based feature descriptor, the Local Directional Pattern Variance (LDPv), which characterizes both the texture and contrast information of facial components. The LDPv descriptor is a collection of Local Directional Pattern (LDP) codes weighted by their corresponding variances. The feature dimension is then reduced by extracting the most discriminative elements of the representation with Principal Component Analysis (PCA). The recognition performance based on our LDPv descriptor has been evaluated using Cohn-Kanade expression database with a Support Vector Machine (SVM) classifier. The discriminative strength of LDPv representation is also assesse...

Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - MB '10, 2010

Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims

Facial Expression conveys non-verbal cues, which plays important roles in interpersonal relations. The Facial Expression Recognition system is the process of identifying the emotional state of a person. In this system captured image is compared with the trained dataset available in database and then emotional state of the image will be displayed. This system is based on image processing and machine learning. For designing a robust facial feature descriptor, we apply the Local Binary Pattern. Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The histogram will be formed by using the operator label of LBP. The recognition performance of the proposed method will be evaluated by using the trained database with the help of Support Vector Machine. Experimental results with prototypic expressions show the superiority of the LBP descriptor against some well-known appearance-based feature representation methods. We evaluate our proposed method on the JAFFE and COHN-KANADE dataset. The Precision, Recall and Fscore from the COHN-KANADE dataset were 83.6142%, 95.0822% and 88.9955% respectively and that of JAFFE dataset were 91.8986%, 98.3649%, 95.0218% respectively. Experimental results demonstrate the competitive classification accuracy of our proposed method.

2015

This paper presents a face expression recognition algorithm using Constrained Local Model (CLM). CLM is facial alignment method that is based on Active Shape Models (ASM) and Active Appearance Models (AAM). It takes the advantages of both of them and gains high accuracy. To distinguish different expression states, we use CLM model parameters that describe shape deformation in a compact form. These parameters form feature vectors for training Kernel Support Vector Machine (KSVM) classifier. The experimental results over Cohn-Kanade Extended Facial Expression (CK+) database show improvement of the recognition rate in comparison to some existing methods, suggested by other authors.

Journal of Electronic Imaging, 2012

A methodology for automatic facial expression recognition in image sequences is proposed, which makes use of the Candide wire frame model and an active appearance algorithm for tracking, and support vector machine (SVM) for classification. A face is detected automatically from the given image sequence and by adapting the Candide wire frame model properly on the first frame of face image sequence, facial features in the subsequent frames are tracked using an active appearance algorithm. The algorithm adapts the Candide wire frame model to the face in each of the frames and then automatically tracks the grid in consecutive video frames over time. We require that first frame of the image sequence corresponds to the neutral facial expression, while the last frame of the image sequence corresponds to greatest intensity of facial expression. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the SVM, which classify the facial expression into one of the classes viz happy, surprise, sadness, anger, disgust, and fear.

2010 13th International Conference on Computer and Information Technology (ICCIT), 2010

The changing process of facial expression contains dynamic texture information, and its accurate extraction is crucial to expression recognition. In this paper, a weighted adaptive symmetric center binary pattern from three orthogonal planes (ASCBP-TOP) is proposed for facial expression recognition. First, facial expression image sequences are partitioned into sub-blocks on different scales to establish a multi-scale space, and ASCBP-TOP is used to extract the dynamic texture features of facial expression image sequences in each scale space and obtain the corresponding feature histograms. Then, these feature histograms are connected in series with different weights to obtain an overall feature histogram for describing the dynamic texture features of facial expression sequences. Finally, support vector machine (SVM) is used to classify and recognize facial expressions. Experiments on Cohn-Kanade and JAFFE databases show that the proposed method is superior to the state-of-the-art methods, and it can extract the dynamic texture information more effectively. In addition, the proposed method is more robust to illumination, expression and pose variations, and it has higher expression recognition rate.

Facial expressions are widely used for the development of several research fields, such as human-computer interfaces, human emotion analysis, biometric and entertainment. This paper proposed a method for facial expression recognition. need to talk more about FER. Randomized tree method is used to detect and localize facial feature keypoint. Each facial feature keypoint is then re-present using Speeded-Up Robust Feature (SURF) descriptor. need to add info about descriptor that has been used. This SURF descriptor is used as facial feature vector for expression classification scheme to recognize seven different facial expression, such as neutral, angry, disgust, fear, happy, sad and surprise. Experiment result show that the recognition rate for SVM classifier methods is 85.3571% using only normalized geometric position of set of fiducial points and 91.6031% if all of our features is used.