Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features

2021 ◽  
pp. 1-21
Author(s):  
Tehmina Kalsum ◽  
Zahid Mehmood ◽  
Farzana Kulsoom ◽  
Hassan Nazeer Chaudhry ◽  
AR Khan ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Texture Features ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Proposed Model ◽  
Image Blurring ◽  
Local Intensity ◽  
Order Pattern

Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.

scholarly journals Recognition of Symbolic Gestures Using Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hasan Mahmud ◽  
Md. Kamrul Hasan ◽  
Abdullah-Al-Tariq ◽  
Md. Hasanul Kabir ◽  
M. A. Mottalib
Keyword(s):  
Contextual Information ◽  
Depth Map ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Depth Information ◽  
Scale Invariant ◽  
Binary Images ◽  
Depth Images ◽  
Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 269-281 ◽  
Author(s):  
Hima Bindu ◽  
Manjunathachari K.
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition System ◽  
Feature Descriptor ◽  
Global Features ◽  
Fuzzy Classifier ◽  
Content Type ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
Proposed Model

Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

2021 ◽  
pp. 6787-6794
Author(s):  
Anisha Rebinth, Dr. S. Mohan Kumar
Keyword(s):  
Principal Component Analysis ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

scholarly journals Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

2020 ◽  
Vol 9 (2) ◽  
pp. 109 ◽  
Author(s):  
Bo Cheng ◽  
Shiai Cui ◽  
Xiaoxiao Ma ◽  
Chenbin Liang
Keyword(s):  
High Resolution ◽  
Extraction Method ◽  
Texture Features ◽  
Decomposition Methods ◽  
Projection Matrix ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Building Area ◽  
Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

2020 ◽  
Author(s):  
Thamba Meshach W ◽  
Hemajothi S ◽  
Mary Anita E A
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Technologies ◽  
Support Vector ◽  
Affect Recognition ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Hybrid Features ◽  
Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals Audio-Visual Emotion Recognition System using Multi-Modal Features

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Emotion Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Support Vector ◽  
Facial Features ◽  
Expression Recognition ◽  
Challenging Problem ◽  
Visual Models ◽  
Proposed Model ◽  
Linear Svm

Due to the highly variant face geometry and appearances, Facial Expression Recognition (FER) is still a challenging problem. CNN can characterize 2-D signals. Therefore, for emotion recognition in a video, the authors propose a feature selection model in AlexNet architecture to extract and filter facial features automatically. Similarly, for emotion recognition in audio, the authors use a deep LSTM-RNN. Finally, they propose a probabilistic model for the fusion of audio and visual models using facial features and speech of a subject. The model combines all the extracted features and use them to train the linear SVM (Support Vector Machine) classifiers. The proposed model outperforms the other existing models and achieves state-of-the-art performance for audio, visual and fusion models. The model classifies the seven known facial expressions, namely anger, happy, surprise, fear, disgust, sad, and neutral on the eNTERFACE’05 dataset with an overall accuracy of 76.61%.

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