Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition

Mandal, Murari; Verma, Monu; Mathur, Sonakshi; Vipparthi, Santosh Kumar; Murala, Subrahmanyam; Deveerasetty, Kranthi Kumar

doi:10.1049/iet-ipr.2018.5683

Cited by 47 publications

(27 citation statements)

References 47 publications

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“…Meanwhile, based on JAFFE, CK+, and MMI databases, we found that our method was in general better than recent studies on facial expression recognition. Our method improved the accuracy of facial expression recognition based on JAFFE database; results of recognition rate based on CK+ database indicated that our method was not as good as the previous methods but outperformed [ 17 ] which used similar experiment. Recognition rate based on MMI database suggested the proposed method achieved superior recognition accuracy than all four state-of-the-art approaches.…”

Section: Methodsmentioning

confidence: 78%

“…Mandal et al extracted the adaptive positional thresholds for a facial image; threshold parameters in the local neighborhood can be adaptively adjusted for different images; then multidistance magnitude features are encoded. SVM is used as a classifier for the facial expression recognition [ 17 ]. Tsai combined the Haar-like features method with the self-quotient image (SQI) filter for facial expression recognition.…”

Section: Related Workmentioning

confidence: 99%

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Facial Expression Recognition with LBP and ORB Features

Niu

Gao

Guo

2021

Computational Intelligence and Neuroscience

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Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.

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Section: Methodsmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition with LBP and ORB Features

Niu

Gao

Guo

2021

Computational Intelligence and Neuroscience

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“…Research suggests that about 55% of human communication happens through facial expressions only [8]. Since the last decade, the field of Facial Expression Recognition (FER) has gathered a lot of attention from researchers because of its wide range of applications in driver mood detection, affective computing, clinical psychology and animation [35] etc. The relevance in day to day communication and advanced intelligent interactions between humans and machines are the important factors driving the study of FER [45].…”

Section: Introductionmentioning

confidence: 99%

“…So, developing a feature descriptor that is robust to such dynamic changes is a complicated and challenging task. FER system includes image acquisition and pre-processing task, feature extraction task for extracting expression-specific features and classification task for classifying the expressions [35]. The classification task of an FER system is hugely dependent on the method used for feature extraction, as inappropriate feature extraction would degrade the performance even after using the best of classifiers.…”

Section: Introductionmentioning

confidence: 99%

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Kartheek

Prasad

Bhukya

2021

Complex Intell. Syst.

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Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

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“…It is a spontaneous expression. e above properties of microexpressions make it a window to understand human real feelings [4,5]. erefore, microexpressions have many potential applications, such as criminal investigation, national defense security, clinical diagnosis, and humancomputer interaction.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network

Wang

2020

Mathematical Problems in Engineering

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In order to recognize the instantaneous changes of facial microexpressions in natural environment, a method based on optical flow direction histogram and depth multiview network to enhance forest microexpression recognition was proposed. In the preprocessing stage, the histogram equalization of the acquired face image is performed, and then the dense key points of the face are detected. According to the coordinates of the key points and the face action coding system (FACS), the face region is divided into 15 regions of interest (ROI). In the feature extraction stage, the optical flow direction histogram feature between adjacent frames in ROI is extracted to detect the peak frame of microexpression sequence. Finally, the average optical flow direction histogram feature of the image sequence from the initial frame to the peak frame is extracted. In the classification stage, firstly, the head pose parameters under horizontal degrees of freedom are estimated to eliminate the influence of head pose motion, and a forest multiview conditional probability model based on deep multiview network is established. Conditional probability and neural connection function are introduced into the node splitting learning of random tree to improve the learning ability and distinguishing ability of the model on the limited training set. Finally, multiview-weighted voting is used to determine the categories of facial microexpressions. Experiments on CASME II microexpression dataset show that the proposed method can effectively describe the changes of microexpressions and improve the recognition accuracy compared with other new methods.

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Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition

Cited by 47 publications

References 47 publications

Facial Expression Recognition with LBP and ORB Features

Facial Expression Recognition with LBP and ORB Features

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network

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