Texture based Emotion Recognition from Facial Expressions using Support Vector Machine

Punitha, A.; Geetha, M. Kalaiselvi

doi:10.5120/13854-1715

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…Statistical and features that are taken from GLPM are given as the input for classification to SVM. The detection rate is identified as 90% [4]. The algorithm detects the characteristic points of a victimization abstraction filter technique by the data given by author [5].…”

Section: Texture-basedmentioning

confidence: 99%

Analysis on techniques used to recognize and identifying the Human emotions

Kulkarni¹,

Rajesh²

2020

IJECE

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Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress of research in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify the proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on the various recognition techniques used to identify the complexity in recognizing the facial expression is presented. This work will also help researchers and scholars to ease out the problem in choosing the techniques used in the identification of the facial expression domain.

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Section: Texture-basedmentioning

confidence: 99%

Analysis on techniques used to recognize and identifying the Human emotions

Kulkarni¹,

Rajesh²

2020

IJECE

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“…In this section, we have elaborated on few research articles which detects human facial expressions. Punitha et al [20] proposed a technique to recognise the facial expressions using texture information on face and trained the texture features with support vector machine (SVM) to classify the facial expressions. Schiavenato et al [21] study contributes pain recognition in the RGB video of infants using computer enabled point pair and NFSC facial action methods.…”

Section: Facial Expressions and Pain Recognitionmentioning

confidence: 99%

“…Punitha et al . [20] proposed a technique to recognise the facial expressions using texture information on face and trained the texture features with support vector machine (SVM) to classify the facial expressions. Schiavenato et al .…”

Section: Facial Expressions and Pain Recognitionmentioning

confidence: 99%

Scope of physiological and behavioural pain assessment techniques in children – a review

Subramaniam

Doss

Chanderasekar

et al. 2018

Healthcare Technology Letters

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Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.

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“…Data on the direction of a data point

, the larger the values of x shows a corresponding increase on the Gram Matrix

. Studies have shown polynomial kernels have been applied to overcome challenges in various fields, such as person re-identification problem by correctly matching person image from a set of gallery of person images in the field of machine learning [ 21 ], new malware detection in cyber-security [ 22 ], big data classification [ 23 ], facial recognition on multiple face images [ 24 ], human body movement and posture recognition in the medical field [ 25 ], facial emotion recognition [ 26 ] and classification of human brain images against mental health conditions in medical imaging [ 27 ]. Radial Basic Function (RBF Kernel), also known as the Gaussian Similarity Kernel defined as

.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks

Muriira

Zhao

Min

2018

Sensors

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Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.

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Texture based Emotion Recognition from Facial Expressions using Support Vector Machine

Cited by 11 publications

References 21 publications

Analysis on techniques used to recognize and identifying the Human emotions

Analysis on techniques used to recognize and identifying the Human emotions

Scope of physiological and behavioural pain assessment techniques in children – a review

Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks

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