Japanese Female Facial Expression Research Articles

An Approach to Detect the Region of Interest of Expressive Face Images

On human face, non-rigid facial movements due to facial expressions cause noticeable alterations in their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper presents an automatic Region of Interest (ROI) detection technique of six universal expressive face images. The proposed technique is a facial geometric based hybrid approach. The localization accuracy was evaluated by rectangular error measure and was tested on Japanese Female Facial Expression (JAFFE) database. The average localization accuracy of all detected facial regions is 94%.

Using multiple steerable filters and Bayesian regularization for facial expression recognition

Facial expression recognition has recently become a challenging research area. Its applications include human–computer interfaces, human emotion analysis, and medical care and cure.In this paper, we present a new challenging method to recognize seven universal emotional expressions, which are happiness, neutral, angry, disgust, sadness, fear and surprise. In our approach, we identify the user׳s facial expressions from the input images, using statistical features extracted from the steerable pyramid decomposition, and classified with a Bayesian regularization neural network. The evaluation of the proposed approach in terms of recognition accuracy is achieved using two universal databases, the Japanese Female Facial Expression database and the Cohn–Kanade facial expression database. The overall accuracy rate reaches 93.33% for the first database and is about 98.13% for the second one. These results show the effectiveness of the steerable proposed algorithm.

Signed Local Directional Pattern을 이용한 강력한 얼굴 표정인식

본 논문에서는 얼굴 표정인식을 위한 새로운 지역 미세 패턴 기술 방법인 Signed Local Directional Pattern(SLDP)을 제안한다. SLDP는 얼굴 영상의 텍스쳐 정보를 표현하기 위해 에지 정보를 이용한다. 이는 기존의 방법들에 비해 뛰어난 구별 성능과 효율적인 코드 생성을 가능하게 한다. SLDP는 마스크 범위 이웃 화소들을 이용하여 에지 반응 값을 계산하고 이들 중 부호를 고려하여 에지 반응 값이 큰 에지 방향 정보를 가지고 만들어진다. 이는 기존 LDP에서 구별하지 못하던 비슷한 에지구조에 밝기 값이 반대인 지역 패턴을 구별할 수 있다. 본 논문에서는 얼굴 표정인식을 위해 얼굴 영상을 여러 영역으로 분할하고 각 영역으로부터 SLDP코드의 분포를 계산한다. 각 분포는 얼굴의 지역적인 특징을 나타내고 이들 특징을 연결해서 얼굴 전체를 나타내는 얼굴 특징 벡터를 생성한다. 본 논문에서는 생성된 얼굴 특징 벡터와 SVM(Support Vector Machine)을 이용해서 Cohn-Kanade 데이터베이스와 JAFFE데이터베이스에서 얼굴 표정인식을 수행했다. SLDP는 표정인식에서 기존 방법들보다 뛰어난 결과를 보여주었다.

Improved Combination of LBP plus LFDA for Facial Expression Recognition using SRC

Human facial expression is one of the most powerful, natural and immediate means for communication between each other. Automatic human facial expression recognition is challenging, interesting problem in many areas such as human computer interaction and data driven animation etc. In this paper, Facial expression based on Local Binary Pattern (LBP) is evaluated, “curse of dimensionality” for real world scenarios problem solved by dimensionality reduction using Local Fisher Discriminant Analysis (LFDA) and Sparse representation classifier (SRC) used for efficient facial expression classification. The experiment is performed in both person-independent and person-dependent facial expression recognition cases, on Japanese Female Facial Expression (JAFFE) and observed that LBP features perform stably and robustly over useful range of low resolutions of face images (150 by 110 pixel and 64 by 64 pixel size) . Proposed method shows better result than traditional algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and LBP+SRC solely. General Terms Pattern recognition, Image processing, Biometric

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS) sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP) and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE) database. Compared with other widely used methods such as linear support vector machines (SVM), sparse representation-based classifier (SRC), nearest subspace classifier (NSC), K-nearest neighbor (KNN) and radial basis function neural networks (RBFNN), the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.

Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation

This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.

Hybrid feature extraction for facial emotion recognition

In recent advances of technology, the application of facial emotion recognition for human-computer interaction (HCI) is becoming an emerging trend. This HCI depends to a large extent on its ability to recognise the facial expression and ability to withstand various kinds of noise. However, confidence in its ability in providing adequate recognition remains challenging due to subtlety, complexity, and variability of facial expression. Therefore, this paper proposes hybrid feature extraction using 2D discrete wavelet transform (DWT), principal component analysis (PCA), and linear discriminant analysis (LDA) for seven (angry, disgust, fear, happy, neutral, sad, and surprise) different facial emotion recognition from static images. The reduced features are tested using k-nearest-neighbour classifier to recognise the facial emotion. The proposed method is evaluated based on two different databases, namely Japanese female facial expression and Cohn-Kanade database. The proposed method gives promising recognition rates of 100% for JAFFE and 97.52% for Cohn-Kanade. The effect of different wavelet families on the classification performance is also investigated.

Automatic recognition of facial movement for paralyzed face.

Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.

Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF

Recognizing patterns by radial basis function network using reduced features obtained through wavelet transformation and discriminative common vector is proposed. Wavelet coefficients obtained after applying wavelet transformations on input patterns, are used to extract significant features from the samples. The discriminative common vectors are extracted using the within-class scatter matrix method from the wavelet coefficients. The discriminative common vectors are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. The proposed method reduces the number of features, minimizes the computational complexity and provides better recognition rates.

Using the genetic algorithm to enhance nonnegative matrix factorization initialization

AbstractNonnegative matrix factorization (NMF) algorithms have been utilized in a wide range of real applications; however, the performance of NMF is highly dependent on three factors including: (1) choosing a problem dependent cost function; (2) using an effective initialization method to start the updating procedure from a near‐optimal point; and (3) determining the rank of factorized matrices prior to decomposition. Due to the nonconvex nature of the NMF cost function, finding an analytical‐based optimal solution is impossible. This paper is aimed at proposing an efficient initialization method to modify the NMF performance. To widely explore the search space for initializing the factorized matrices in NMF, the island genetic algorithm (IGA) is employed as a diverse multiagent search scheme. To adapt IGA for NMF initialization, we present a specific mutation operator. To assess how the proposed IGA initialization method efficiently enhances NMF performance, we have implemented state‐of‐the‐art initialization methods and applied to the Japanese Female Facial Expression dataset to recognize the facial expression states. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of relative error and fast convergence.

A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION

Texture feature plays a predominant role in recognizing face images. However different persons can have similar texture features that may degrade the system performance. Hence in this paper, the problem of face similarity is addressed by proposing a solution which combines textural and geometrical features. An algorithm is proposed to combine these two features. Five texture descriptors and few geometrical features are considered to validate the proposed system. Performance evaluations of these features are carried out independently and jointly for three different issues such as expression variation, illumination variation and partial occlusion with objects. It is observed that the combination of textural and geometrical features enhance the accuracy of face recognition. Experimental results on Japanese Female Facial Expression (JAFFE) and ESSEX databases indicate that the texture descriptor Local Binary Pattern achieves better recognition accuracy for all the issues considered.

Facial Expression Recognition using Gabor Wavelet

expression recognition (FER) has good applications in different aspects of day-to-day life. But not yet realized due to unavailability of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction by using feed-forward neural networks (classifier) for recognition of four different facial expressions from still pictures of the human face. The study presented here gives simple method in facial expression recognition. The study presented here gives 72.50% recognition of facial expression for the entire database of JAFEE. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions. KeywordsGABOR, FER, JAFFE.

Hybrid Facial Geometry Algorithm for facial feature Extraction and Expression Recognition using ANFIS and BPNN

An Intelligent Biometrics systems aims at localizing and detecting human faces from supplied images so that further recognition of persons and their facial expression recognition will be easy. The area of human-computer interaction (HCI) will be much more effective if a computer is able to recognize the emotional state of human being. Emotional states have a greater effect on the face which can tell about mood of a person. So if we can recognize facial expressions, we will know something about the human’s emotions and mood. This paper focuses on the novel Hybrid Facial Geometry Algorithm (HFGA) and comparative analysis of Facial Geometry algorithm and HFGA for facial feature extraction and its use to classify facial expressions. Feed forward back propagation neural network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as classifiers for expression classification and recognition. Experimentations are carried out using Japanese Female Facial Expression (JAFFE) database. Experimental results shows that average recognition efficiency from 95.33% to 93.33% is achieved for 30 to 75 test samples using BPNN and 95.71% to 95.33% with ANFIS approach.

Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks

An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.

A Neural Network based Facial Expression Recognition using Fisherface

expression plays significant role for human beings to communicate their emotions. Automatic facial expression analysis is a flourishing area of research in computer science, and it is also still a challenge. This paper discusses the application of a natural network based facial expression recognition using fisherface. Back propagation neural network is used as a classifier for classifying the expressions. For face portion segmentation and localization, integral projection method is used. The accuracy of system performance have evaluated on a public database Japanese Female Facial Expression (JAFFE). The experimental results show the effectiveness of our scheme. The best average recognition rate achieves 89.20%.

Facial Expression Recognition Using New Feature Extraction Algorithm

This paper proposes a method for facial expression recognition. Facial feature vectors are generated from keypoint descriptors using Speeded-Up Robust Features. Each facial feature vector is then normalized and next the probability density function descriptor is generated. The distance between two probability density function descriptors is calculated using Kullback Leibler divergence. Mathematical equation is employed to select certain practicable probability density function descriptors for each grid, which are used as the initial classification. Subsequently, the corresponding weight of the class for each grid is determined using a weighted majority voting classifier. The class with the largest weight is output as the recognition result. The proposed method shows excellent performance when applied to the Japanese Female Facial Expression database.

Facial Expressions Recognition Using Eigenspaces

A challenging research topic is to make the Computer Systems to recognize facial expressions from the face image. A method of facial expression recognition, based on Eigenspaces is presented in this study. Here, the authors recognize the user’s facial expressions from the input images, using a method that was customized from eigenface recognition. Evaluation was done for this method in terms of identification correctness using two different Facial Expressions databases, Cohn-Kanade facial expression database and Japanese Female Facial Expression database. The results show the effectiveness of proposed method.

Local binary pattern and its derivatives for face recognition

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

Face Representation Using Combined Method of Gabor Filters, Wavelet Transformation and DCV and Recognition Using RBF

An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.